prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What argument components do the ML methods aim to identify?
Context 1: Introduction
The art of argumentation has been studied since the early work of Aristotle, dating back to the 4th century BC BIBREF0   It has been exhaustively examined from different perspectives, such as philosophy, psychology, communication studies, cognitive science, formal and informal logic, linguistics, computer science, educational research, and many others  In a recent and critically well-acclaimed study, Mercier Sperber 2011 even claim that argumentation is what drives humans to perform reasoning  From the pragmatic perspective, argumentation can be seen as a verbal activity oriented towards the realization of a goal BIBREF1 or more in detail as a verbal, social, and rational activity aimed at convincing a reasonable critic of the acceptability of a standpoint by putting forward a constellation of one or more propositions to justify this standpoint BIBREF2  
Analyzing argumentation from the computational linguistics point of view has very recently led to a new field called argumentation mining BIBREF3   Despite the lack of an exact definition, researchers within this field usually focus on analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and analyze textual data at hand 
Our motivation for argumentation mining stems from a practical information seeking perspective from the user-generated content on the Web  For example, when users search for information in user-generated Web content to facilitate their personal decision making related to controversial topics, they lack tools to overcome the current information overload  One particular use-case example dealing with a forum post discussing private versus public schools is shown in Figure FIGREF4   Here, the lengthy text on the left-hand side is transformed into an argument gist on the right-hand side by (i) analyzing argument components and (ii) summarizing their content  Figure FIGREF5 shows another use-case example, in which users search for reasons that underpin certain standpoint in a given controversy (which is homeschooling in this case)  In general, the output of automatic argument analysis performed on the large scale in Web data can provide users with analyzed arguments to a given topic of interest, find the evidence for the given controversial standpoint, or help to reveal flaws in argumentation of others 
Satisfying the above-mentioned information needs cannot be directly tackled by current methods for, e g , opinion mining, questions answering, or summarization and requires novel approaches within the argumentation mining field  Although user-generated Web content has already been considered in argumentation mining, many limitations and research gaps can be identified in the existing works  First, the scope of the current approaches is restricted to a particular domain or register, e g , hotel reviews BIBREF5 , Tweets related to local riot events BIBREF6 , student essays BIBREF7 , airline passenger rights and consumer protection BIBREF8 , or renewable energy sources BIBREF9   Second, not all the related works are tightly connected to argumentation theories, resulting into a gap between the substantial research in argumentation itself and its adaptation in NLP applications  Third, as an emerging research area, argumentation mining still suffers from a lack of labeled corpora, which is crucial for designing, training, and evaluating the algorithms  Although some works have dealt with creating new data sets, the reliability (in terms of inter-annotator agreement) of the annotated resources is often unknown BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14  
Annotating and automatically analyzing arguments in unconstrained user-generated Web discourse represent challenging tasks  So far, the research in argumentation mining “has been conducted on domains like news articles, parliamentary records and legal documents, where the documents contain well-formed explicit arguments, i e , propositions with supporting reasons and evidence present in the text” BIBREF8   [p  50]Boltuzic Snajder 2014 point out that “unlike in debates or other more formal argumentation sources, the arguments provided by the users, if any, are less formal, ambiguous, vague, implicit, or often simply poorly worded ” Another challenge stems from the different nature of argumentation theories and computational linguistics  Whereas computational linguistics is mainly descriptive, the empirical research that is carried out in argumentation theories does not constitute a test of the theoretical model that is favored, because the model of argumentation is a normative instrument for assessing the argumentation BIBREF15   So far, no fully fledged descriptive argumentation theory based on empirical research has been developed, thus feasibility of adapting argumentation models to the Web discourse represents an open issue 
These challenges can be formulated into the following research questions:
In this article, we push the boundaries of the argumentation mining field by focusing on several novel aspects  We tackle the above-mentioned research questions as well as the previously discussed challenges and issues  First, we target user-generated Web discourse from several domains across various registers, to examine how argumentation is communicated in different contexts  Second, we bridge the gap between argumentation theories and argumentation mining through selecting the argumenation model based on research into argumentation theories and related fields in communication studies or psychology  In particular, we adapt normative models from argumentation theory to perform empirical research in NLP and support our application of argumentation theories with an in-depth reliability study  Finally, we use state-of-the-art NLP techniques in order to build robust computational models for analyzing arguments that are capable of dealing with a variety of genres on the Web 
Our contributions
We create a new corpus which is, to the best of our knowledge, the largest corpus that has been annotated within the argumentation mining field to date  We choose several target domains from educational controversies, such as homeschooling, single-sex education, or mainstreaming  A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments to articles, discussion forum posts, blog posts, as well as professional newswire articles 
Since the data come from a variety of sources and no assumptions about its actual content with respect to argumentation can be drawn, we conduct two extensive annotation studies  In the first study, we tackle the problem of relatively high “noise” in the retrieved data  In particular, not all of the documents are related to the given topics in a way that makes them candidates for further deep analysis of argumentation (this study results into 990 annotated documents)  In the second study, we discuss the selection of an appropriate argumentation model based on evidence in argumentation research and propose a model that is suitable for analyzing micro-level argumention in user-generated Web content  Using this model, we annotate 340 documents (approx  90,000 tokens), reaching a substantial inter-annotator agreement  We provide a hand-analysis of all the phenomena typical to argumentation that are prevalent in our data  These findings may also serve as empirical evidence to issues that are on the spot of current argumentation research 
From the computational perspective, we experiment on the annotated data using various machine learning methods in order to extract argument structure from documents  We propose several novel feature sets and identify configurations that run best in in-domain and cross-domain scenarios  To foster research in the community, we provide the annotated data as well as all the experimental software under free license 
The rest of the article is structured as follows  First, we provide an essential background in argumentation theory in section SECREF2   Section SECREF3 surveys related work in several areas  Then we introduce the dataset and two annotation studies in section SECREF4   Section SECREF5 presents our experimental work and discusses the results and errors and section SECREF6 concludes this article 
Theoretical background
Let us first present some definitions of the term argumentation itself  [p  3]Ketcham 1917 defines argumentation as “the art of persuading others to think or act in a definite way  It includes all writing and speaking which is persuasive in form ” According to MacEwan 1898, “argumentation is the process of proving or disproving a proposition  Its purpose is to induce a new belief, to establish truth or combat error in the mind of another ” [p  2]Freeley Steinberg 2008 narrow the scope of argumentation to “reason giving in communicative situations by people whose purpose is the justification of acts, beliefs, attitudes, and values ” Although these definitions vary, the purpose of argumentation remains the same – to persuade others 
We would like to stress that our perception of argumentation goes beyond somehow limited giving reasons BIBREF17 , BIBREF18   Rather, we see the goal of argumentation as to persuade BIBREF19 , BIBREF20 , BIBREF21   Persuasion can be defined as a successful intentional effort at influencing another's mental state through communication in a circumstance in which the persuadee has some measure of freedom BIBREF22 , although, as OKeefe2011 points out, there is no correct or universally-endorsed definition of either `persuasion' or `argumentation'  However, broader understanding of argumentation as a means of persuasion allows us to take into account not only reasoned discourse, but also non-reasoned mechanisms of influence, such as emotional appeals BIBREF23  
Having an argument as a product within the argumentation process, we should now define it  One typical definition is that an argument is a claim supported by reasons BIBREF24   The term claim has been used since 1950's, introduced by Toulmin 1958, and in argumentation theory it is a synonym for standpoint or point of view  It refers to what is an issue in the sense what is being argued about  The presence of a standpoint is thus crucial for argumentation analysis  However, the claim as well as other parts of the argument might be implicit; this is known as enthymematic argumentation, which is rather usual in ordinary argumentative discourse BIBREF25  
One fundamental problem with the definition and formal description of arguments and argumentation is that there is no agreement even among argumentation theorists  As [p  29]vanEmeren et al 2014 admit in their very recent and exhaustive survey of the field, ”as yet, there is no unitary theory of argumentation that encompasses the logical, dialectical, and rhetorical dimensions of argumentation and is universally accepted  The current state of the art in argumentation theory is characterized by the coexistence of a variety of theoretical perspectives and approaches, which differ considerably from each other in conceptualization, scope, and theoretical refinement ”
Argumentation models
Despite the missing consensus on the ultimate argumentation theory, various argumentation models have been proposed that capture argumentation on different levels  Argumentation models abstract from the language level to a concept level that stresses the links between the different components of an argument or how arguments relate to each other BIBREF26   Bentahar et al 2010 propose a taxonomy of argumentation models, that is horizontally divided into three categories – micro-level models, macro-level models, and rhetorical models 
In this article, we deal with argumentation on the micro-level (also called argumentation as a product or monological models)  Micro-level argumentation focuses on the structure of a single argument  By contrast, macro-level models (also called dialogical models) and rhetorical models highlight the process of argumentation in a dialogue BIBREF27   In other words, we examine the structure of a single argument produced by a single author in term of its components, not the relations that can exist among arguments and their authors in time  A detailed discussion of these different perspectives can be found, e g , in BIBREF28 , BIBREF29 , BIBREF30 , BIBREF1 , BIBREF31 , BIBREF32  
Dimensions of argument
The above-mentioned models focus basically only on one dimension of the argument, namely the logos dimension  According to the classical Aristotle's theory BIBREF0 , argument can exist in three dimensions, which are logos, pathos, and ethos  Logos dimension represents a proof by reason, an attempt to persuade by establishing a logical argument  For example, syllogism belongs to this argumentation dimension BIBREF34 , BIBREF25   Pathos dimension makes use of appealing to emotions of the receiver and impacts its cognition BIBREF35   Ethos dimension of argument relies on the credibility of the arguer  This distinction will have practical impact later in section SECREF51 which deals with argumentation on the Web 
Original Toulmin's model
We conclude the theoretical section by presenting one (micro-level) argumentation model in detail – a widely used conceptual model of argumentation introduced by Toulmin 1958, which we will henceforth denote as the Toulmin's original model  This model will play an important role later in the annotation studies (section SECREF51 ) and experimental work (section SECREF108 )  The model consists of six parts, referred as argument components, where each component plays a distinct role 
is an assertion put forward publicly for general acceptance BIBREF38 or the conclusion we seek to establish by our arguments BIBREF17  
It is the evidence to establish the foundation of the claim BIBREF24 or, as simply put by Toulmin, “the data represent what we have to go on ” BIBREF37   The name of this concept was later changed to grounds in BIBREF38  
The role of warrant is to justify a logical inference from the grounds to the claim 
is a set of information that stands behind the warrant, it assures its trustworthiness 
limits the degree of certainty under which the argument should be accepted  It is the degree of force which the grounds confer on the claim in virtue of the warrant BIBREF37  
presents a situation in which the claim might be defeated 
A schema of the Toulmin's original model is shown in Figure FIGREF29   The lines and arrows symbolize implicit relations between the components  An example of an argument rendered using the Toulmin's scheme can be seen in Figure FIGREF30  
We believe that this theoretical overview should provide sufficient background for the argumentation mining research covered in this article; for further references, we recommend for example BIBREF15  
Related work in computational linguistics
We structure the related work into three sub-categories, namely argumentation mining, stance detection, and persuasion and on-line dialogs, as these areas are closest to this article's focus  For a recent overview of general discourse analysis see BIBREF39   Apart from these, research on computer-supported argumentation has been also very active; see, e g , BIBREF40 for a survey of various models and argumentation formalisms from the educational perspective or BIBREF41 which examines argumentation in the Semantic Web 
Argumentation Mining
The argumentation mining field has been evolving very rapidly in the recent years, resulting into several workshops co-located with major NLP conferences  We first present related works with a focus on annotations and then review experiments with classifying argument components, schemes, or relations 
One of the first papers dealing with annotating argumentative discourse was Argumentative Zoning for scientific publications BIBREF42   Later, Teufel et al 2009 extended the original 7 categories to 15 and annotated 39 articles from two domains, where each sentence is assigned a category  The obtained Fleiss' INLINEFORM0 was 0 71 and 0 65  In their approach, they tried to deliberately ignore the domain knowledge and rely only on general, rhetorical and logical aspect of the annotated texts  By contrast to our work, argumentative zoning is specific to scientific publications and has been developed solely for that task 
Reed Rowe 2004 presented Araucaria, a tool for argumentation diagramming which supports both convergent and linked arguments, missing premises (enthymemes), and refutations  They also released the AracuariaDB corpus which has later been used for experiments in the argumentation mining field  However, the creation of the dataset in terms of annotation guidelines and reliability is not reported – these limitations as well as its rather small size have been identified BIBREF10  
Biran Rambow 2011 identified justifications for subjective claims in blog threads and Wikipedia talk pages  The data were annotated with claims and their justifications reaching INLINEFORM0 0 69, but a detailed description of the annotation approach was missing 
[p  1078]Schneider et al 2013b annotated Wikipedia talk pages about deletion using 17 Walton's schemes BIBREF43 , reaching a moderate agreement (Cohen's INLINEFORM0 0 48) and concluded that their analysis technique can be reused, although “it is intensive and difficult to apply ”
Stab Gurevych 2014 annotated 90 argumentative essays (about 30k tokens), annotating claims, major claims, and premises and their relations (support, attack)  They reached Krippendorff's INLINEFORM0 0 72 for argument components and Krippendorff's INLINEFORM1 0 81 for relations between components 
Rosenthal2012 annotated sentences that are opinionated claims, in which the author expresses a belief that should be adopted by others  Two annotators labeled sentences as claims without any context and achieved Cohen's INLINEFORM0 0 50 (2,000 sentences from LiveJournal) and 0 56 (2,000 sentences from Wikipedia) 
Aharoni et al 2014 performed an annotation study in order to find context-dependent claims and three types of context-dependent evidence in Wikipedia, that were related to 33 controversial topics  The claim and evidence were annotated in 104 articles  The average Cohen's INLINEFORM0 between a group of 20 expert annotators was 0 40  Compared to our work, the linguistic properties of Wikipedia are qualitatively different from other user-generated content, such as blogs or user comments BIBREF44  
Wacholder et al 2014 annotated “argument discourse units” in blog posts and criticized the Krippendorff's INLINEFORM0 measure  They proposed a new inter-annotator metric by taking the most overlapping part of one annotation as the “core” and all annotations as a “cluster”  The data were extended by Ghosh2014, who annotated “targets” and “callouts” on the top of the units 
Park Cardie 2014 annotated about 10k sentences from 1,047 documents into four types of argument propositions with Cohen's INLINEFORM0 0 73 on 30% of the dataset  Only 7% of the sentences were found to be non-argumentative 
Faulkner2014 used Amazon Mechanical Turk to annotate 8,179 sentences from student essays  Three annotators decided whether the given sentence offered reasons for or against the main prompt of the essay (or no reason at all; 66% of the sentences were found to be neutral and easy to identify)  The achieved Cohen's INLINEFORM0 was 0 70 
The research has also been active on non-English datasets  Goudas et al 2014 focused on user-generated Greek texts  They selected 204 documents and manually annotated sentences that contained an argument (760 out of 16,000)  They distinguished claims and premises, but the claims were always implicit  However, the annotation agreement was not reported, neither was the number of annotators or the guidelines  A study on annotation of arguments was conducted by Peldszus Stede 2013, who evaluate agreement among 26 “naive" annotators (annotators with very little training)  They manually constructed 23 German short texts, each of them contains exactly one central claim, two premises, and one objection (rebuttal or undercut) and analyzed annotator agreement on this artificial data set  Peldszus 2014 later achieved higher inter-rater agreement with expert annotators on an extended version of the same data  Kluge 2014 built a corpus of argumentative German Web documents, containing 79 documents from 7 educational topics, which were annotated by 3 annotators according to the claim-premise argumentation model  The corpus comprises 70,000 tokens and the inter-annotator agreement was 0 40 (Krippendorff's INLINEFORM0 )  Houy et al 2013 targeted argumentation mining of German legal cases 
Table TABREF33 gives an overview of annotation studies with their respective argumentation model, domain, size, and agreement  It also contains other studies outside of computational linguistics and few proposals and position papers 
Arguments in the legal domain were targeted in BIBREF11   Using argumentation formalism inspired by Walton 2012, they employed multinomial Naive Bayes classifier and maximum entropy model for classifying argumentative sentences on the AraucariaDB corpus BIBREF45   The same test dataset was used by Feng Hirst 2011, who utilized the C4 5 decision classifier  Rooney et al 2012 investigated the use of convolution kernel methods for classifying whether a sentence belongs to an argumentative element or not using the same corpus 
Stab Gurevych 2014b classified sentences to four categories (none, major claim, claim, premise) using their previously annotated corpus BIBREF7 and reached 0 72 macro- INLINEFORM0 score  In contrast to our work, their documents are expected to comply with a certain structure of argumentative essays and are assumed to always contain argumentation 
Biran Rambow 2011 identified justifications on the sentence level using a naive Bayes classifier over a feature set based on statistics from the RST Treebank, namely n-grams which were manually processed by deleting n-grams that “seemed irrelevant, ambiguous or domain-specific ”
Llewellyn2014 experimented with classifying tweets into several argumentative categories, namely claims and counter-claims (with and without evidence) and verification inquiries previously annotated by Procter et al 2013  They used unigrams, punctuations, and POS as features in three classifiers 
Park Cardie 2014 classified propositions into three classes (unverifiable, verifiable non-experimental, and verifiable experimental) and ignored non-argumentative texts  Using multi-class SVM and a wide range of features (n-grams, POS, sentiment clue words, tense, person) they achieved Macro INLINEFORM0 0 69 
Peldszus 2014 experimented with a rather complex labeling schema of argument segments, but their data were artificially created for their task and manually cleaned, such as removing segments that did not meet the criteria or non-argumentative segments 
In the first step of their two-phase approach, Goudas et al 2014 sampled the dataset to be balanced and identified argumentative sentences with INLINEFORM0 0 77 using the maximum entropy classifier  For identifying premises, they used BIO encoding of tokens and achieved INLINEFORM1 score 0 42 using CRFs 
Saint-Dizier 2012 developed a Prolog engine using a lexicon of 1300 words and a set of 78 hand-crafted rules with the focus on a particular argument structure “reasons supporting conclusions” in French 
Taking the dialogical perspective, Cabrio Villata 2012 built upon an argumentation framework proposed by Dung 1995 which models arguments within a graph structure and provides a reasoning mechanism for resolving accepted arguments  For identifying support and attack, they relied on existing research on textual entailment BIBREF46 , namely using the off-the-shelf EDITS system  The test data were taken from a debate portal Debatepedia and covered 19 topics  Evaluation was performed in terms of measuring the acceptance of the “main argument" using the automatically recognized entailments, yielding INLINEFORM0 score about 0 75  By contrast to our work which deals with micro-level argumentation, the Dung's model is an abstract framework intended to model dialogical argumentation 
Finding a bridge between existing discourse research and argumentation has been targeted by several researchers  Peldszus2013a surveyed literature on argumentation and proposed utilization of Rhetorical Structure Theory (RST) BIBREF47   They claimed that RST is by its design well-suited for studying argumentative texts, but an empirical evidence has not yet been provided  Penn Discourse Tree Bank (PDTB) BIBREF48 relations have been under examination by argumentation mining researchers too  Cabrio2013b examined a connection between five Walton's schemes and discourse markers in PDTB, however an empirical evaluation is missing 
Stance detection
Research related to argumentation mining also involves stance detection  In this case, the whole document (discussion post, article) is assumed to represent the writer's standpoint to the discussed topic  Since the topic is stated as a controversial question, the author is either for or against it 
Somasundaran Wiebe 2009 built a computational model for recognizing stances in dual-topic debates about named entities in the electronic products domain by combining preferences learned from the Web data and discourse markers from PDTB BIBREF48   Hasan Ng 2013 determined stance in on-line ideological debates on four topics using data from createdebate com, employing supervised machine learning and features ranging from n-grams to semantic frames  Predicting stance of posts in Debatepedia as well as external articles using a probabilistic graphical model was presented in BIBREF49   This approach also employed sentiment lexicons and Named Entity Recognition as a preprocessing step and achieved accuracy about 0 80 in binary prediction of stances in debate posts 
Recent research has involved joint modeling, taking into account information about the users, the dialog sequences, and others  Hasan Ng 2012 proposed machine learning approach to debate stance classification by leveraging contextual information and author's stances towards the topic  Qiu et al 2013 introduced a computational debate side model to cluster posts or users by sides for general threaded discussions using a generative graphical model employing words from various subjectivity lexicons as well as all adjectives and adverbs in the posts  Qiu Jiang 2013 proposed a graphical model for viewpoint discovery in discussion threads  Burfoot et al 2011 exploited the informal citation structure in U S  Congressional floor-debate transcripts and use a collective classification which outperforms methods that consider documents in isolation 
Some works also utilize argumentation-motivated features  Park et al 2011 dealt with contentious issues in Korean newswire discourse  Although they annotate the documents with “argument frames”, the formalism remains unexplained and does not refer to any existing research in argumentation  Walker et al 2012b incorporated features with some limited aspects of the argument structure, such as cue words signaling rhetorical relations between posts, POS generalized dependencies, and a representation of the parent post (context) to improve stance classification over 14 topics from convinceme net 
Online persuasion
Another stream of research has been devoted to persuasion in online media, which we consider as a more general research topic than argumentation 
Schlosser 2011 investigated persuasiveness of online reviews and concluded that presenting two sides is not always more helpful and can even be less persuasive than presenting one side  Mohammadi et al 2013 explored persuasiveness of speakers in YouTube videos and concluded that people are perceived more persuasive in video than in audio and text  Miceli et al 2006 proposed a computational model that attempts to integrate emotional and non-emotional persuasion  In the study of Murphy 2001, persuasiveness was assigned to 21 articles (out of 100 manually preselected) and four of them are later analyzed in detail for comparing the perception of persuasion between expert and students  Bernard et al 2012 experimented with children's perception of discourse connectives (namely with “because”) to link statements in arguments and found out that 4- and 5-years-old and adults are sensitive to the connectives  Le 2004 presented a study of persuasive texts and argumentation in newspaper editorials in French 
A coarse-grained view on dialogs in social media was examined by Bracewell et al 2013, who proposed a set of 15 social acts (such as agreement, disagreement, or supportive behavior) to infer the social goals of dialog participants and presented a semi-supervised model for their classification  Their social act types were inspired by research in psychology and organizational behavior and were motivated by work in dialog understanding  They annotated a corpus in three languages using in-house annotators and achieved INLINEFORM0 in the range from 0 13 to 0 53 
Georgila et al 2011 focused on cross-cultural aspects of persuasion or argumentation dialogs  They developed a novel annotation scheme stemming from different literature sources on negotiation and argumentation as well as from their original analysis of the phenomena  The annotation scheme is claimed to cover three dimensions of an utterance, namely speech act, topic, and response or reference to a previous utterance  They annotated 21 dialogs and reached Krippendorff's INLINEFORM0 between 0 38 and 0 57 
Given the broad landscape of various approaches to argument analysis and persuasion studies presented in this section, we would like to stress some novel aspects of the current article  First, we aim at adapting a model of argument based on research by argumentation scholars, both theoretical and empirical  We pose several pragmatical constraints, such as register independence (generalization over several registers)  Second, our emphasis is put on reliable annotations and sufficient data size (about 90k tokens)  Third, we deal with fairly unrestricted Web-based sources, so additional steps of distinguishing whether the texts are argumentative are required  Argumentation mining has been a rapidly evolving field with several major venues in 2015  We encourage readers to consult an upcoming survey article by Lippi Torroni 2016 or the proceedings of the 2nd Argumentation Mining workshop BIBREF50 to keep up with recent developments  However, to the best of our knowledge, the main findings of this article have not yet been made obsolete by any related work 
Annotation studies and corpus creation
This section describes the process of data selection, annotation, curation, and evaluation with the goal of creating a new corpus suitable for argumentation mining research in the area of computational linguistics  As argumentation mining is an evolving discipline without established and widely-accepted annotation schemes, procedures, and evaluation, we want to keep this overview detailed to ensure full reproducibility of our approach  Given the wide range of perspectives on argumentation itself BIBREF15 , variety of argumentation models BIBREF27 , and high costs of discourse or pragmatic annotations BIBREF48 , creating a new, reliable corpus for argumentation mining represents a substantial effort 
A motivation for creating a new corpus stems from the various use-cases discussed in the introduction, as well as some research gaps pointed in section SECREF1 and further discussed in the survey in section SECREF31 (e g , domain restrictions, missing connection to argumentation theories, non-reported reliability or detailed schemes) 
Topics and registers
As a main field of interest in the current study, we chose controversies in education  One distinguishing feature of educational topics is their breadth of sub-topics and points of view, as they attract researchers, practitioners, parents, students, or policy-makers  We assume that this diversity leads to the linguistic variability of the education topics and thus represents a challenge for NLP  In a cooperation with researchers from the German Institute for International Educational Research we identified the following current controversial topics in education in English-speaking countries: (1) homeschooling, (2) public versus private schools, (3) redshirting — intentionally delaying the entry of an age-eligible child into kindergarten, allowing their child more time to mature emotionally and physically BIBREF51 , (4) prayer in schools — whether prayer in schools should be allowed and taken as a part of education or banned completely, (5) single-sex education — single-sex classes (males and females separate) versus mixed-sex classes (“co-ed”), and (6) mainstreaming — including children with special needs into regular classes 
Since we were also interested in whether argumentation differs across registers, we included four different registers — namely (1) user comments to newswire articles or to blog posts, (2) posts in discussion forums (forum posts), (3) blog posts, and (4) newswire articles  Throughout this work, we will refer to each article, blog post, comment, or forum posts as a document  This variety of sources covers mainly user-generated content except newswire articles which are written by professionals and undergo an editing procedure by the publisher  Since many publishers also host blog-like sections on their portals, we consider as blog posts all content that is hosted on personal blogs or clearly belong to a blog category within a newswire portal 
Raw corpus statistics
Given the six controversial topics and four different registers, we compiled a collection of plain-text documents, which we call the raw corpus  It contains 694,110 tokens in 5,444 documents  As a coarse-grained analysis of the data, we examined the lengths and the number of paragraphs (see Figure FIGREF43 )  Comments and forum posts follow a similar distribution, being shorter than 300 tokens on average  By contrast, articles and blogs are longer than 400 tokens and have 9 2 paragraphs on average  The process of compiling the raw corpus and its further statistics are described in detail in Appendix UID158  
Annotation study 1: Identifying persuasive documents in forums and comments
The goal of this study was to select documents suitable for a fine-grained analysis of arguments  In a preliminary study on annotating argumentation using a small sample (50 random documents) of forum posts and comments from the raw corpus, we found that many documents convey no argumentation at all, even in discussions about controversies  We observed that such contributions do not intend to persuade; these documents typically contain story-sharing, personal worries, user interaction (asking questions, expressing agreement), off-topic comments, and others  Such characteristics are typical to on-line discussions in general, but they have not been examined with respect to argumentation or persuasion  Indeed, we observed that there are (1) documents that are completely unrelated and (2) documents that are related to the topic, but do not contain any argumentation  This issue has been identified among argumentation theorist; for example as external relevance by Paglieri Castelfranchia 2014  Similar findings were also confirmed in related literature in argumentation mining, however never tackled empirically BIBREF53 , BIBREF8 These documents are thus not suitable for analyzing argumentation 
In order to filter documents that are suitable for argumentation annotation, we defined a binary document-level classification task  The distinction is made between either persuasive documents or non-persuasive (which includes all other sorts of texts, such as off-topic, story sharing, unrelated dialog acts, etc ) 
The two annotated categories were on-topic persuasive and non-persuasive  Three annotators with near-native English proficiency annotated a set of 990 documents (a random subset of comments and forum posts) reaching 0 59 Fleiss' INLINEFORM0   The final label was selected by majority voting  The annotation study took on average of 15 hours per annotator with approximately 55 annotated documents per hour  The resulting labels were derived by majority voting  Out of 990 documents, 524 (53%) were labeled as on-topic persuasive  We will refer to this corpus as gold data persuasive 
We examined all disagreements between annotators and discovered some typical problems, such as implicitness or topic relevance  First, the authors often express their stance towards the topic implicitly, so it must be inferred by the reader  To do so, certain common-ground knowledge is required  However, such knowledge heavily depends on many aspects, such as the reader's familiarity with the topic or her cultural background, as well as the context of the source website or the discussion forum thread  This also applies for sarcasm and irony  Second, the decision whether a particular topic is persuasive was always made with respect to the controversial topic under examination  Some authors shift the focus to a particular aspect of the given controversy or a related issue, making the document less relevant 
We achieved moderate agreement between the annotators, although the definition of persuasiveness annotation might seem a bit fuzzy  We found different amounts of persuasion in the specific topics  For instance, prayer in schools or private vs  public schools attract persuasive discourse, while other discussed controversies often contain non-persuasive discussions, represented by redshirting and mainstreaming  Although these two topics are also highly controversial, the participants of on-line discussions seem to not attempt to persuade but they rather exchange information, support others in their decisions, etc  This was also confirmed by socio-psychological researchers  Ammari et al 2014 show that parents of children with special needs rely on discussion sites for accessing information and social support and that, in particular, posts containing humor, achievement, or treatment suggestions are perceived to be more socially appropriate than posts containing judgment, violence, or social comparisons  According to Nicholson Leask 2012, in the online forum, parents of autistic children were seen to understand the issue because they had lived it  Assuming that participants in discussions related to young kids (e g , redshirting, or mainstreaming) are usually females (mothers), the gender can also play a role  In a study of online persuasion, Guadagno Cialdini 2002 conclude that women chose to bond rather than compete (women feel more comfortable cooperating, even in a competitive environment), whereas men are motivated to compete if necessary to achieve independence 
Annotation study 2: Annotating micro-structure of arguments
The goal of this study was to annotate documents on a detailed level with respect to an argumentation model  First, we will present the annotation scheme  Second, we will describe the annotation process  Finally, we will evaluate the agreement and draw some conclusions 
Given the theoretical background briefly introduced in section SECREF2 , we motivate our selection of the argumentation model by the following requirements  First, the scope of this work is to capture argumentation within a single document, thus focusing on micro-level models  Second, there should exist empirical evidence that such a model has been used for analyzing argumentation in previous works, so it is likely to be suitable for our purposes of argumentative discourse analysis in user-generated content  Regarding the first requirement, two typical examples of micro-level models are the Toulmin's model BIBREF36 and Walton's schemes BIBREF55   Let us now elaborate on the second requirement 
Walton's argumentation schemes are claimed to be general and domain independent  Nevertheless, evidence from the computational linguistics field shows that the schemes lack coverage for analyzing real argumentation in natural language texts  In examining real-world political argumentation from BIBREF56 , Walton 2012 found out that 37 1% of the arguments collected did not fit any of the fourteen schemes they chose so they created new schemes ad-hoc  Cabrio2013b selected five argumentation schemes from Walton and map these patterns to discourse relation categories in the Penn Discourse TreeBank (PDTB) BIBREF48 , but later they had to define two new argumentation schemes that they discovered in PDTB  Similarly, Song et al 2014 admitted that the schemes are ambiguous and hard to directly apply for annotation, therefore they modified the schemes and created new ones that matched the data 
Although Macagno Konstantinidou 2012 show several examples of two argumentation schemes applied to few selected arguments in classroom experiments, empirical evidence presented by Anthony Kim 2014 reveals many practical and theoretical difficulties of annotating dialogues with schemes in classroom deliberation, providing many details on the arbitrary selection of the sub-set of the schemes, the ambiguity of the scheme definitions, concluding that the presence of the authors during the experiment was essential for inferring and identifying the argument schemes BIBREF57  
Although this model (refer to section SECREF21 ) was designed to be applicable to real-life argumentation, there are numerous studies criticizing both the clarity of the model definition and the differentiation between elements of the model  Ball1994 claims that the model can be used only for the most simple arguments and fails on the complex ones  Also Freeman1991 and other argumentation theorists criticize the usefulness of Toulmin's framework for the description of real-life argumentative texts  However, others have advocated the model and claimed that it can be applied to the people's ordinary argumenation BIBREF58 , BIBREF59  
A number of studies (outside the field of computational linguistics) used Toulmin's model as their backbone argumentation framework  Chambliss1995 experimented with analyzing 20 written documents in a classroom setting in order to find the argument patterns and parts  Simosi2003 examined employees' argumentation to resolve conflicts  Voss2006 analyzed experts' protocols dealing with problem-solving 
The model has also been used in research on computer-supported collaborative learning  Erduran2004 adapt Toulmin's model for coding classroom argumentative discourse among teachers and students  Stegmann2011 builds on a simplified Toulmin's model for scripted construction of argument in computer-supported collaborative learning  Garcia-Mila2013 coded utterances into categories from Toulmin's model in persuasion and consensus-reaching among students  Weinberger Fischer 2006 analyze asynchronous discussion boards in which learners engage in an argumentative discourse with the goal to acquire knowledge  For coding the argument dimension, they created a set of argumentative moves based on Toulmin's model  Given this empirical evidence, we decided to build upon the Toulmin's model 
In this annotation task, a sequence of tokens (e g  a phrase, a sentence, or any arbitrary text span) is labeled with a corresponding argument component (such as the claim, the grounds, and others)  There are no explicit relations between these annotation spans as the relations are implicitly encoded in the pragmatic function of the components in the Toulmin's model 
In order to prove the suitability of the Toulmin's model, we analyzed 40 random documents from the gold data persuasive dataset using the original Toulmin's model as presented in section SECREF21   We took into account sever criteria for assessment, such as frequency of occurrence of the components or their importance for the task  We proposed some modifications of the model based on the following observations 
Authors do not state the degree of cogency (the probability of their claim, as proposed by Toulmin)  Thus we omitted qualifier from the model due to its absence in the data 
The warrant as a logical explanation why one should accept the claim given the evidence is almost never stated  As pointed out by BIBREF37 , “data are appealed to explicitly, warrants implicitly ” This observation has also been made by Voss2006  Also, according to [p  205]Eemeren et al 1987, the distinction of warrant is perfectly clear only in Toulmin’s examples, but the definitions fail in practice  We omitted warrant from the model 
Rebuttal is a statement that attacks the claim, thus playing a role of an opposing view  In reality, the authors often attack the presented rebuttals by another counter-rebuttal in order to keep the whole argument's position consistent  Thus we introduced a new component – refutation – which is used for attacking the rebuttal  Annotation of refutation was conditioned of explicit presence of rebuttal and enforced by the annotation guidelines  The chain rebuttal–refutation is also known as the procatalepsis figure in rhetoric, in which the speaker raises an objection to his own argument and then immediately answers it  By doing so, the speaker hopes to strengthen the argument by dealing with possible counter-arguments before the audience can raise them BIBREF43  
The claim of the argument should always reflect the main standpoint with respect to the discussed controversy  We observed that this standpoint is not always explicitly expressed, but remains implicit and must be inferred by the reader  Therefore, we allow the claim to be implicit  In such a case, the annotators must explicitly write down the (inferred) stance of the author 
By definition, the Toulmin's model is intended to model single argument, with the claim in its center  However, we observed in our data, that some authors elaborate on both sides of the controversy equally and put forward an argument for each side (by argument here we mean the claim and its premises, backings, etc )  Therefore we allow multiple arguments to be annotated in one document  At the same time, we restrained the annotators from creating complex argument hierarchies 
Toulmin's grounds have an equivalent role to a premise in the classical view on an argument BIBREF15 , BIBREF60 in terms that they offer the reasons why one should accept the standpoint expressed by the claim  As this terminology has been used in several related works in the argumentation mining field BIBREF7 , BIBREF61 , BIBREF62 , BIBREF11 , we will keep this convention and denote the grounds as premises 
One of the main critiques of the original Toulmin's model was the vague distinction between grounds, warrant, and backing BIBREF63 , BIBREF64 , BIBREF65   The role of backing is to give additional support to the warrant, but there is no warrant in our model anymore  However, what we observed during the analysis, was a presence of some additional evidence  Such evidence does not play the role of the grounds (premises) as it is not meant as a reason supporting the claim, but it also does not explain the reasoning, thus is not a warrant either  It usually supports the whole argument and is stated by the author as a certain fact  Therefore, we extended the scope of backing as an additional support to the whole argument 
The annotators were instructed to distinguish between premises and backing, so that premises should cover generally applicable reasons for the claim, whereas backing is a single personal experience or statements that give credibility or attribute certain expertise to the author  As a sanity check, the argument should still make sense after removing backing (would be only considered “weaker”) 
We call the model as a modified Toulmin's model  It contains five argument components, namely claim, premise, backing, rebuttal, and refutation  When annotating a document, any arbitrary token span can be labeled with an argument component; the components do not overlap  The spans are not known in advance and the annotator thus chooses the span and the component type at the same time  All components are optional (they do not have to be present in the argument) except the claim, which is either explicit or implicit (see above)  If a token span is not labeled by any argument component, it is not considered as a part of the argument and is later denoted as none (this category is not assigned by the annotators) 
An example analysis of a forum post is shown in Figure FIGREF65   Figure FIGREF66 then shows a diagram of the analysis from that example (the content of the argument components was shortened or rephrased) 
The annotation experiment was split into three phases  All documents were annotated by three independent annotators, who participated in two training sessions  During the first phase, 50 random comments and forum posts were annotated  Problematic cases were resolved after discussion and the guidelines were refined  In the second phase, we wanted to extend the range of annotated registers, so we selected 148 comments and forum posts as well as 41 blog posts  After the second phase, the annotation guidelines were final 
In the final phase, we extended the range of annotated registers and added newswire articles from the raw corpus in order to test whether the annotation guidelines (and inherently the model) is general enough  Therefore we selected 96 comments/forum posts, 8 blog posts, and 8 articles for this phase  A detailed inter-annotator agreement study on documents from this final phase will be reported in section UID75  
The annotations were very time-consuming  In total, each annotator spent 35 hours by annotating in the course of five weeks  Discussions and consolidation of the gold data took another 6 hours  Comments and forum posts required on average of 4 minutes per document to annotate, while blog posts and articles on average of 14 minutes per document  Examples of annotated documents from the gold data are listed in Appendix UID158  
We discarded 11 documents out of the total 351 annotated documents  Five forum posts, although annotated as persuasive in the first annotation study, were at a deeper look a mixture of two or more posts with missing quotations, therefore unsuitable for analyzing argumentation  Three blog posts and two articles were found not to be argumentative (the authors took no stance to the discussed controversy) and one article was an interview, which the current model cannot capture (a dialogical argumentation model would be required) 
For each of the 340 documents, the gold standard annotations were obtained using the majority vote  If simple majority voting was not possible (different boundaries of the argument component together with a different component label), the gold standard was set after discussion among the annotators  We will refer to this corpus as the gold standard Toulmin corpus  The distribution of topics and registers in this corpus in shown in Table TABREF71 , and Table TABREF72 presents some lexical statistics 
Based on pre-studies, we set the minimal unit for annotation as token  The documents were pre-segmented using the Stanford Core NLP sentence splitter BIBREF69 embedded in the DKPro Core framework BIBREF70   Annotators were asked to stick to the sentence level by default and label entire pre-segmented sentences  They should switch to annotations on the token level only if (a) a particular sentence contained more than one argument component, or (b) if the automatic sentence segmentation was wrong  Given the “noise” in user-generated Web data (wrong or missing punctuation, casing, etc ), this was often the case 
Annotators were also asked to rephrase (summarize) each annotated argument component into a simple statement when applicable, as shown in Figure FIGREF66   This was used as a first sanity checking step, as each argument component is expected to be a coherent discourse unit  For example, if a particular occurrence of a premise cannot be summarized/rephrased into one statement, this may require further splitting into two or more premises 
For the actual annotations, we developed a custom-made web-based application that allowed users to switch between different granularity of argument components (tokens or sentences), to annotate the same document in different argument “dimensions” (logos and pathos), and to write summary for each annotated argument component 
As a measure of annotation reliability, we rely on Krippendorff's unitized alpha ( INLINEFORM0 ) BIBREF71   To the best of our knowledge, this is the only agreement measure that is applicable when both labels and boundaries of segments are to be annotated 
Although the measure has been used in related annotation works BIBREF61 , BIBREF7 , BIBREF72 , there is one important detail that has not been properly communicated  The INLINEFORM0 is computed over a continuum of the smallest units, such as tokens  This continuum corresponds to a single document in the original Krippendorff's work  However, there are two possible extensions to multiple documents (a corpus), namely (a) to compute INLINEFORM1 for each document first and then report an average value, or (b) to concatenate all documents into one large continuum and compute INLINEFORM2 over it  The first approach with averaging yielded extremely high the standard deviation of INLINEFORM3 (i e , avg  = 0 253; std  dev  = 0 886; median = 0 476 for the claim)  This says that some documents are easy to annotate while others are harder, but interpretation of such averaged value has no evidence either in BIBREF71 or other papers based upon it  Thus we use the other methodology and treat the whole corpus as a single long continuum (which yields in the example of claim 0 541 INLINEFORM4 ) 
Table TABREF77 shows the inter-annotator agreement as measured on documents from the last annotation phase (see section UID67 )  The overall INLINEFORM0 for all register types, topics, and argument components is 0 48 in the logos dimension (annotated with the modified Toulmin's model)  Such agreement can be considered as moderate by the measures proposed by Landis Koch 1977, however, direct interpretation of the agreement value lacks consensus BIBREF54   Similar inter-annotator agreement numbers were achieved in the relevant works in argumentation mining (refer to Table TABREF33 in section SECREF31 ; although most of the numbers are not directly comparable, as different inter-annotator metrics were used on different tasks) 
There is a huge difference in INLINEFORM0 regarding the registers between comments + forums posts ( INLINEFORM1 0 60, Table TABREF77 a) and articles + blog posts ( INLINEFORM2 0 09, Table TABREF77 b) in the logos dimension  If we break down the value with respect to the individual argument components, the agreement on claim and premise is substantial in the case of comments and forum posts (0 59 and 0 69, respectively)  By contrast, these argument components were annotated only with a fair agreement in articles and blog posts (0 22 and 0 24, respectively) 
As can be also observed from Table TABREF77 , the annotation agreement in the logos dimension varies regarding the document topic  While it is substantial/moderate for prayer in schools (0 68) or private vs  public schools (0 44), for some topics it remains rather slight, such as in the case of redshirting (0 14) or mainstreaming (0 08) 
First, we examine the disagreement in annotations by posing the following research question: are there any measurable properties of the annotated documents that might systematically cause low inter-annotator agreement? We use Pearson's correlation coefficient between INLINEFORM0 on each document and the particular property under investigation  We investigated the following set of measures 
Full sentence coverage ratio represents a ratio of argument component boundaries that are aligned to sentence boundaries  The value is 1 0 if all annotations in the particular document are aligned to sentences and 0 0 if no annotations match the sentence boundaries  Our hypothesis was that automatic segmentation to sentences was often incorrect, therefore annotators had to switch to the token level annotations and this might have increased disagreement on boundaries of the argument components 
Document length, paragraph length and average sentence length  Our hypotheses was that the length of documents, paragraphs, or sentences negatively affects the agreement 
Readability measures  We tested four standard readability measures, namely Ari BIBREF73 , Coleman-Liau BIBREF74 , Flesch BIBREF75 , and Lix BIBREF76 to find out whether readability of the documents plays any role in annotation agreement 
Correlation results are listed in Table TABREF82   We observed the following statistically significant ( INLINEFORM0 ) correlations  First, document length negatively correlates with agreement in comments  The longer the comment was the lower the agreement was  Second, average paragraph length negatively correlates with agreement in blog posts  The longer the paragraphs in blogs were, the lower agreement was reached  Third, all readability scores negatively correlate with agreement in the public vs  private school domain, meaning that the more complicated the text in terms of readability is, the lower agreement was reached  We observed no significant correlation in sentence coverage and average sentence length measures  We cannot draw any general conclusion from these results, but we can state that some registers and topics, given their properties, are more challenging to annotate than others 
Another qualitative analysis of disagreements between annotators was performed by constructing a probabilistic confusion matrix BIBREF77 on the token level  The biggest disagreements, as can be seen in Table TABREF85 , is caused by rebuttal and refutation confused with none (0 27 and 0 40, respectively)  This is another sign that these two argument components were very hard to annotate  As shown in Table TABREF77 , the INLINEFORM5 was also low – 0 08 for rebuttal and 0 17 for refutation 
We analyzed the annotations and found the following phenomena that usually caused disagreements between annotators 
Each argument component (e g , premise or backing) should express one consistent and coherent piece of information, for example a single reason in case of the premise (see Section UID73 )  However, the decision whether a longer text should be kept as a single argument component or segmented into multiple components is subjective and highly text-specific 
While rhetorical questions have been researched extensively in linguistics BIBREF78 , BIBREF79 , BIBREF80 , BIBREF81 , their role in argumentation represents a substantial research question BIBREF82 , BIBREF83 , BIBREF84 , BIBREF85 , BIBREF86   Teninbaum 2011 provides a brief history of rhetorical questions in persuasion  In short, rhetorical questions should provoke the reader  From the perspective of our argumentation model, rhetorical questions might fall both into the logos dimension (and thus be labeled as, e g , claim, premise, etc ) or into the pathos dimension (refer to Section SECREF20 )  Again, the decision is usually not clear-cut 
As introduced in section UID55 , rebuttal attacks the claim by presenting an opponent's view  In most cases, the rebuttal is again attacked by the author using refutation  From the pragmatic perspective, refutation thus supports the author's stance expressed by the claim  Therefore, it can be easily confused with premises, as the function of both is to provide support for the claim  Refutation thus only takes place if it is meant as a reaction to the rebuttal  It follows the discussed matter and contradicts it  Such a discourse is usually expressed as:
[claim: My claim ] [rebuttal: On the other hand, some people claim XXX which makes my claim wrong ] [refutation: But this is not true, because of YYY ]
However, the author might also take the following defensible approach to formulate the argument:
[rebuttal: Some people claim XXX-1 which makes my claim wrong ] [refutation: But this is not true, because of YYY-1 ] [rebuttal: Some people claim XXX-2 which makes my claim wrong ] [refutation: But this is not true, because of YYY-2 ] [claim: Therefore my claim ]
If this argument is formulated without stating the rebuttals, it would be equivalent to the following:
[premise: YYY-1 ] [premise: YYY-2 ] [claim: Therefore my claim ]
This example shows that rebuttal and refutation represent a rhetorical device to produce arguments, but the distinction between refutation and premise is context-dependent and on the functional level both premise and refutation have very similar role – to support the author's standpoint  Although introducing dialogical moves into monological model and its practical consequences, as described above, can be seen as a shortcoming of our model, this rhetoric figure has been identified by argumentation researchers as procatalepsis BIBREF43   A broader view on incorporating opposing views (or lack thereof) is discussed under the term confirmation bias by BIBREF21 who claim that “[   ] people are trying to convince others  They are typically looking for arguments and evidence to confirm their own claim, and ignoring negative arguments and evidence unless they anticipate having to rebut them ” The dialectical attack of possible counter-arguments may thus strengthen one's own argument 
One possible solution would be to refrain from capturing this phenomena completely and to simplify the model to claims and premises, for instance  However, the following example would then miss an important piece of information, as the last two clauses would be left un-annotated  At the same time, annotating the last clause as premise would be misleading, because it does not support the claim (in fact, it supports it only indirectly by attacking the rebuttal; this can be seen as a support is considered as an admissible extension of abstract argument graph by BIBREF87 ) 
Doc#422 (forumpost, homeschooling) [claim: I try not to be anti-homeschooling, but    it's just hard for me ] [premise: I really haven't met any homeschoolers who turned out quite right, including myself ] I apologize if what I'm saying offends any of you - that's not my intention, [rebuttal: I know that there are many homeschooled children who do just fine,] but [refutation: that hasn't been my experience ]
To the best of our knowledge, these context-dependent dialogical properties of argument components using Toulmin's model have not been solved in the literature on argumentation theory and we suggest that these observations should be taken into account in the future research in monological argumentation 
Appeal to emotion, sarcasm, irony, or jokes are common in argumentation in user-generated Web content  We also observed documents in our data that were purely sarcastic (the pathos dimension), therefore logical analysis of the argument (the logos dimension) would make no sense  However, given the structure of such documents, some claims or premises might be also identified  Such an argument is a typical example of fallacious argumentation, which intentionally pretends to present a valid argument, but its persuasion is conveyed purely for example by appealing to emotions of the reader BIBREF88  
We present some statistics of the annotated data that are important from the argumentation research perspective  Regardless of the register, 48% of claims are implicit  This means that the authors assume that their standpoint towards the discussed controversy can be inferred by the reader and give only reasons for that standpoint  Also, explicit claims are mainly written just once, only in 3% of the documents the claim was rephrased and occurred multiple times 
In 6% of the documents, the reasons for an implicit claim are given only in the pathos dimension, making the argument purely persuasive without logical argumentation 
The “myside bias”, defined as a bias against information supporting another side of an argument BIBREF89 , BIBREF90 , can be observed by the presence of rebuttals to the author's claim or by formulating arguments for both sides when the overall stance is neutral  While 85% of the documents do not consider any opposing side, only 8% documents present a rebuttal, which is then attacked by refutation in 4% of the documents  Multiple rebuttals and refutations were found in 3% of the documents  Only 4% of the documents were overall neutral and presented arguments for both sides, mainly in blog posts 
We were also interested whether mitigating linguistic devices are employed in the annotated arguments, namely in their main stance-taking components, the claims  Such devices typically include parenthetical verbs, syntactic constructions, token agreements, hedges, challenge questions, discourse markers, and tag questions, among others BIBREF91   In particular, [p  1]Kaltenbock et al 2010 define hedging as a discourse strategy that reduces the force or truth of an utterance and thus reduces the risk a speaker runs when uttering a strong or firm assertion or other speech act  We manually examined the use of hedging in the annotated claims 
Our main observation is that hedging is used differently across topics  For instance, about 30-35% of claims in homeschooling and mainstreaming signal the lack of a full commitment to the expressed stance, in contrast to prayer in schools (15%) or public vs  private schools (about 10%)  Typical hedging cues include speculations and modality (“If I have kids, I will probably homeschool them ”), statements as neutral observations (“It's not wrong to hold the opinion that in general it's better for kids to go to school than to be homeschooled ”), or weasel phrases BIBREF92 (“In some cases, inclusion can work fantastically well ”, “For the majority of the children in the school, mainstream would not have been a suitable placement ”) 
On the other hand, most claims that are used for instance in the prayer in schools arguments are very direct, without trying to diminish its commitment to the conveyed belief (for example, “NO PRAYER IN SCHOOLS!    period ”, “Get it out of public schools”, “Pray at home ”, or “No organized prayers or services anywhere on public school board property - FOR ANYONE ”)  Moreover, some claims are clearly offensive, persuading by direct imperative clauses towards the opponents/audience (“TAKE YOUR KIDS PRIVATE IF YOU CARE AS I DID”, “Run, don't walk, to the nearest private school ”) or even accuse the opponents for taking a certain stance (“You are a bad person if you send your children to private school ”) 
These observations are consistent with the findings from the first annotation study on persuasion (see section UID48 ), namely that some topics attract heated argumentation where participant take very clear and reserved standpoints (such as prayer in schools or private vs  public schools), while discussions about other topics are rather milder  It has been shown that the choices a speaker makes to express a position are informed by their social and cultural background, as well as their ability to speak the language BIBREF93 , BIBREF94 , BIBREF91   However, given the uncontrolled settings of the user-generated Web content, we cannot infer any similar conclusions in this respect 
We investigated premises across all topics in order to find the type of support used in the argument  We followed the approach of Park Cardie 2014, who distinguished three types of propositions in their study, namely unverifiable, verifiable non-experiential, and verifiable experiential 
Verifiable non-experiential and verifiable experiential propositions, unlike unverifiable propositions, contain an objective assertion, where objective means “expressing or dealing with facts or conditions as perceived without distortion by personal feelings, prejudices, or interpretations ” Such assertions have truth values that can be proved or disproved with objective evidence; the correctness of the assertion or the availability of the objective evidence does not matter BIBREF8   A verifiable proposition can further be distinguished as experiential or not, depending on whether the proposition is about the writer's personal state or experience or something non-experiential  Verifiable experiential propositions are sometimes referred to as anectotal evidence, provide the novel knowledge that readers are seeking BIBREF8  
Table TABREF97 shows the distribution of the premise types with examples for each topic from the annotated corpus  As can be seen in the first row, arguments in prayer in schools contain majority (73%) of unverifiable premises  Closer examination reveals that their content vary from general vague propositions to obvious fallacies, such as a hasty generalization, straw men, or slippery slope  As Nieminen Mustonen 2014 found out, fallacies are very common in argumentation about religion-related issues  On the other side of the spectrum, arguments about redshirting rely mostly on anecdotal evidence (61% of verifiable experiential propositions)  We will discuss the phenomena of narratives in argumentation in more detail later in section UID98   All the topics except private vs  public schools exhibit similar amount of verifiable non-experiential premises (9%–22%), usually referring to expert studies or facts  However, this type of premises has usually the lowest frequency 
Manually analyzing argumentative discourse and reconstructing (annotating) the underlying argument structure and its components is difficult  As [p  267]Reed2006 point out, “the analysis of arguments is often hard, not only for students, but for experts too ” According to [p  81]Harrell 2011b, argumentation is a skill and “even for simple arguments, untrained college students can identify the conclusion but without prompting are poor at both identifying the premises and how the premises support the conclusion ” [p  81]Harrell 2011 further claims that “a wide literature supports the contention that the particular skills of understanding, evaluating, and producing arguments are generally poor in the population of people who have not had specific training and that specific training is what improves these skills ” Some studies, for example, show that students perform significantly better on reasoning tasks when they have learned to identify premises and conclusions BIBREF95 or have learned some standard argumentation norms BIBREF96  
One particular extra challenge in analyzing argumentation in Web user-generated discourse is that the authors produce their texts probably without any existing argumentation theory or model in mind  We assume that argumentation or persuasion is inherent when users discuss controversial topics, but the true reasons why people participate in on-line communities and what drives their behavior is another research question BIBREF97 , BIBREF98 , BIBREF99 , BIBREF100   When the analyzed texts have a clear intention to produce argumentative discourse, such as in argumentative essays BIBREF7 , the argumentation is much more explicit and a substantially higher inter-annotator agreement can be achieved 
The model seems to be suitable for short persuasive documents, such as comments and forum posts  Its applicability to longer documents, such as articles or blog posts, is problematic for several reasons 
The argument components of the (modified) Toulmin's model and their roles are not expressive enough to capture argumentation that not only conveys the logical structure (in terms of reasons put forward to support the claim), but also relies heavily on the rhetorical power  This involves various stylistic devices, pervading narratives, direct and indirect speech, or interviews  While in some cases the argument components are easily recognizable, the vast majority of the discourse in articles and blog posts does not correspond to any distinguishable argumentative function in the logos dimension  As the purpose of such discourse relates more to rhetoric than to argumentation, unambiguous analysis of such phenomena goes beyond capabilities of the current argumentation model  For a discussion about metaphors in Toulmin's model of argumentation see, e g , BIBREF102 , BIBREF103  
Articles without a clear standpoint towards the discussed controversy cannot be easily annotated with the model either  Although the matter is viewed from both sides and there might be reasons presented for either of them, the overall persuasive intention is missing and fitting such data to the argumentation framework causes disagreements  One solution might be to break the document down to paragraphs and annotate each paragraph separately, examining argumentation on a different level of granularity 
As introduced in section SECREF20 , there are several dimensions of an argument  The Toulmin's model focuses solely on the logos dimension  We decided to ignore the ethos dimension, because dealing with the author's credibility remains unclear, given the variety of the source web data  However, exploiting the pathos dimension of an argument is prevalent in the web data, for example as an appeal to emotions  Therefore we experimented with annotating appeal to emotions as a separate category independent of components in the logos dimension  We defined some features for the annotators how to distinguish appeal to emotions  Figurative language such as hyperbole, sarcasm, or obvious exaggerating to “spice up” the argument are the typical signs of pathos  In an extreme case, the whole argument might be purely emotional, as in the following example 
Doc#1698 (comment, prayer in schools) [app-to-emot: Prayer being removed from school is just the leading indicator of a nation that is ‘Falling Away’ from Jehovah  [   ] And the disasters we see today are simply God’s finger writing on the wall: Mene, mene, Tekel, Upharsin; that is, God has weighed America in the balances, and we’ve been found wanting  No wonder 50 million babies have been aborted since 1973  [   ]]
We kept annotations on the pathos dimension as simple as possible (with only one appeal to emotions label), but the resulting agreement was unsatisfying ( INLINEFORM0 0 30) even after several annotation iterations  Appeal to emotions is considered as a type of fallacy BIBREF104 , BIBREF18   Given the results, we assume that more carefully designed approach to fallacy annotation should be applied  To the best of our knowledge, there have been very few research works on modeling fallacies similarly to arguments on the discourse level BIBREF105   Therefore the question, in which detail and structure fallacies should be annotated, remains open  For the rest of the paper, we thus focus on the logos dimension solely 
Some of the educational topics under examination relate to young children (e g , redshirting or mainstreaming); therefore we assume that the majority of participants in discussions are their parents  We observed that many documents related to these topics contain narratives  Sometimes the story telling is meant as a support for the argument, but there are documents where the narrative has no intention to persuade and is simply a story sharing 
There is no widely accepted theory of the role of narratives among argumentation scholars  According to Fisher 1987, humans are storytellers by nature, and the “reason” in argumentation is therefore better understood in and through the narratives  He found that good reasons often take the form of narratives  Hoeken Fikkers 2014 investigated how integration of explicit argumentative content into narratives influences issue-relevant thinking and concluded that identifying with the character being in favor of the issue yielded a more positive attitude toward the issue  In a recent research, Bex 2011 proposes an argumentative-narrative model of reasoning with evidence, further elaborated in BIBREF106 ; also Niehaus et al 2012 proposes a computational model of narrative persuasion 
Stemming from another research field, LeytonEscobar2014 found that online community members who use and share narratives have higher participation levels and that narratives are useful tools to build cohesive cultures and increase participation  Betsch et al 2010 examined influencing vaccine intentions among parents and found that narratives carry more weight than statistics 
Summary of annotation studies
This section described two annotation studies that deal with argumentation in user-generated Web content on different levels of detail  In section SECREF44 , we argued for a need of document-level distinction of persuasiveness  We annotated 990 comments and forum posts, reaching moderate inter-annotator agreement (Fleiss' INLINEFORM0 0 59)  Section SECREF51 motivated the selection of a model for micro-level argument annotation, proposed its extension based on pre-study observations, and outlined the annotation set-up  This annotation study resulted into 340 documents annotated with the modified Toulmin's model and reached moderate inter-annotator agreement in the logos dimension (Krippendorff's INLINEFORM1 0 48)  These results make the annotated corpora suitable for training and evaluation computational models and each of these two annotation studies will have their experimental counterparts in the following section 
Experiments
This section presents experiments conducted on the annotated corpora introduced in section SECREF4   We put the main focus on identifying argument components in the discourse  To comply with the machine learning terminology, in this section we will use the term domain as an equivalent to a topic (remember that our dataset includes six different topics; see section SECREF38 ) 
We evaluate three different scenarios  First, we report ten-fold cross validation over a random ordering of the entire data set  Second, we deal with in-domain ten-fold cross validation for each of the six domains  Third, in order to evaluate the domain portability of our approach, we train the system on five domains and test on the remaining one for all six domains (which we report as cross-domain validation) 
Identification of argument components
In the following experiment, we focus on automatic identification of arguments in the discourse  Our approach is based on supervised and semi-supervised machine learning methods on the gold data Toulmin dataset introduced in section SECREF51  
An argument consists of different components (such as premises, backing, etc ) which are implicitly linked to the claim  In principle one document can contain multiple independent arguments  However, only 4% of the documents in our dataset contain arguments for both sides of the issue  Thus we simplify the task and assume there is only one argument per document 
Given the low inter-annotator agreement on the pathos dimension (Table TABREF77 ), we focus solely on recognizing the logical dimension of argument  The pathos dimension of argument remains an open problem for a proper modeling as well as its later recognition 
Since the smallest annotation unit is a token and the argument components do not overlap, we approach identification of argument components as a sequence labeling problem  We use the BIO encoding, so each token belongs to one of the following 11 classes: O (not a part of any argument component), Backing-B, Backing-I, Claim-B, Claim-I, Premise-B, Premise-I, Rebuttal-B, Rebuttal-I, Refutation-B, Refutation-I  This is the minimal encoding that is able to distinguish two adjacent argument components of the same type  In our data, 48% of all adjacent argument components of the same type are direct neighbors (there are no "O" tokens in between) 
We report Macro- INLINEFORM0 score and INLINEFORM1 scores for each of the 11 classes as the main evaluation metric  This evaluation is performed on the token level, and for each token the predicted label must exactly match the gold data label (classification of tokens into 11 classes) 
As instances for the sequence labeling model, we chose sentences rather than tokens  During our initial experiments, we observed that building a sequence labeling model for recognizing argument components as sequences of tokens is too fine-grained, as a single token does not convey enough information that could be encoded as features for a machine learner  However, as discussed in section UID73 , the annotations were performed on data pre-segmented to sentences and annotating tokens was necessary only when the sentence segmentation was wrong or one sentence contained multiple argument components  Our corpus consists of 3899 sentences, from which 2214 sentences (57%) contain no argument component  From the remaining ones, only 50 sentences (1%) have more than one argument component  Although in 19 cases (0 5%) the sentence contains a Claim-Premise pair which is an important distinction from the argumentation perspective, given the overall small number of such occurrences, we simplify the task by treating each sentence as if it has either one argument component or none 
The approximation with sentence-level units is explained in the example in Figure FIGREF112   In order to evaluate the expected performance loss using this approximation, we used an oracle that always predicts the correct label for the unit (sentence) and evaluated it against the true labels (recall that the evaluation against the true gold labels is done always on token level)  We lose only about 10% of macro INLINEFORM0 score (0 906) and only about 2% of accuracy (0 984)  This performance is still acceptable, while allowing to model sequences where the minimal unit is a sentence 
Table TABREF114 shows the distribution of the classes in the gold data Toulmin, where the labeling was already mapped to the sentences  The little presence of rebuttal and refutation (4 classes account only for 3 4% of the data) makes this dataset very unbalanced 
We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling  Each sentence ( INLINEFORM0 ) is represented as a vector of real-valued features 
We defined the following feature sets:
FS0: Baseline lexical features
word uni-, bi-, and tri-grams (binary)
FS1: Structural, morphological, and syntactic features
First and last 3 tokens  Motivation: these tokens may contain discourse markers or other indicators for argument components, such as “therefore” and “since” for premises or “think” and “believe” for claims 
Relative position in paragraph and relative position in document  Motivation: We expect that claims are more likely to appear at the beginning or at the end of the document 
Number of POS 1-3 grams, dependency tree depth, constituency tree production rules, and number of sub-clauses  Based on BIBREF113  
FS2: Topic and sentiment features
30 features taken from a vector representation of the sentence obtained by using Gibbs sampling on LDA model BIBREF114 , BIBREF115 with 30 topics trained on unlabeled data from the raw corpus  Motivation: Topic representation of a sentence might be valuable for detecting off-topic sentences, namely non-argument components 
Scores for five sentiment categories (from very negative to very positive) obtained from Stanford sentiment analyzer BIBREF116   Motivation: Claims usually express opinions and carry sentiment 
FS3: Semantic, coreference, and discourse features
Binary features from Clear NLP Semantic Role Labeler BIBREF117   Namely, we extract agent, predicate + agent, predicate + agent + patient + (optional) negation, argument type + argument value, and discourse marker which are based on PropBank semantic role labels  Motivation: Exploit the semantics of Capturing the semantics of the sentences 
Binary features from Stanford Coreference Chain Resolver BIBREF118 , e g , presence of the sentence in a chain, transition type (i e , nominal–pronominal), distance to previous/next sentences in the chain, or number of inter-sentence coreference links  Motivation: Presence of coreference chains indicates links outside the sentence and thus may be informative, for example, for classifying whether the sentence is a part of a larger argument component 
Results of a PTDB-style discourse parser BIBREF119 , namely the type of discourse relation (explicit, implicit), presence of discourse connectives, and attributions  Motivation: It has been claimed that discourse relations play a role in argumentation mining BIBREF120  
FS4: Embedding features
300 features from word embedding vectors using word embeddings trained on part of the Google News dataset BIBREF121   In particular, we sum up embedding vectors (dimensionality 300) of each word, resulting into a single vector for the entire sentence  This vector is then directly used as a feature vector  Motivation: Embeddings helped to achieve state-of-the-art results in various NLP tasks BIBREF116 , BIBREF122  
Except the baseline lexical features, all feature types are extracted not only for the current sentence INLINEFORM0 , but also for INLINEFORM1 preceding and subsequent sentences, namely INLINEFORM2 , INLINEFORM3 , INLINEFORM4 INLINEFORM5 , INLINEFORM6 , where INLINEFORM7 was empirically set to 4  Each feature is then represented with a prefix to determine its relative position to the current sequence unit 
Let us first discuss the upper bounds of the system  Performance of the three human annotators is shown in the first column of Table TABREF139 (results are obtained from a cumulative confusion matrix)  The overall Macro- INLINEFORM0 score is 0 602 (accuracy 0 754)  If we look closer at the different argument components, we observe that humans are good at predicting claims, premises, backing and non-argumentative text (about 0 60-0 80 INLINEFORM1 ), but on rebuttal and refutation they achieve rather low scores  Without these two components, the overall human Macro- INLINEFORM2 would be 0 707  This trend follows the inter-annotator agreement scores, as discussed in section UID75  
In our experiments, the feature sets were combined in the bottom-up manner, starting with the simple lexical features (FS0), adding structural and syntactic features (FS1), then adding topic and sentiment features (FS2), then features reflecting the discourse structure (FS3), and finally enriched with completely unsupervised latent vector space representation (FS4)  In addition, we were gradually removing the simple features (e g , without lexical features, without syntactic features, etc ) to test the system with more “abstract” feature sets (feature ablation)  The results are shown in Table TABREF139  
The overall best performance (Macro- INLINEFORM0 0 251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets  Classification of non-argumentative text (the "O" class) yields about 0 7 INLINEFORM1 score even in the baseline setting  The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I)  It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder  The very low numbers for rebuttal and refutation have two reasons  First, these two argument components caused many disagreements in the annotations, as discussed in section UID86 , and were hard to recognize for the humans too  Second, these four classes have very few instances in the corpus (about 3 4%, see Table TABREF114 ), so the classifier suffers from the lack of training data 
The results for the in-domain cross validation scenario are shown in Table TABREF140   Similarly to the cross-validation scenario, the overall best results were achieved using the largest feature set (01234)  For mainstreaming and red-shirting, the best results were achieved using only the feature set 4 (embeddings)  These two domains contain also fewer documents, compared to other domains (refer to Table TABREF71 )  We suspect that embeddings-based features convey important information when not enough in-domain data are available  This observation will become apparent in the next experiment 
The cross-domain experiments yield rather poor results for most of the feature combinations (Table TABREF141 )  However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario  These results indicate that embedding features generalize well across domains in our task of argument component identification  We leave investigating better performing vector representations, such as paragraph vectors BIBREF123 , for future work 
Error analysis based on the probabilistic confusion matrix BIBREF124 shown in Table TABREF142 reveals further details  About a half of the instances for each class are misclassified as non-argumentative (the "O" prediction) 
Backing-B is often confused with Premise-B (12%) and Backing-I with Premise-I (23%)  Similarly, Premise-I is misclassified as Backing-I in 9%  This shows that distinguishing between backing and premises is not easy because these two components are similar such that they support the claim, as discussed in section UID86   We can also see that the misclassification is consistent among *-B and *-I tags 
Rebuttal is often misclassified as Premise (28% for Rebuttal-I and 18% for Rebuttal-B; notice again the consistency in *-B and *-I tags)  This is rather surprising, as one would expect that rebuttal would be confused with a claim, because its role is to provide an opposing view 
Refutation-B and Refutation-I is misclassified as Premise-I in 19% and 27%, respectively  This finding confirms the discussion in section UID86 , because the role of refutation is highly context-dependent  In a pragmatic perspective, it is put forward to indirectly support the claim by attacking the rebuttal, thus having a similar function to the premise 
We manually examined miss-classified examples produced the best-performing system to find out which phenomena pose biggest challenges  Properly detecting boundaries of argument components caused problems, as shown in Figure FIGREF146 (a)  This goes in line with the granularity annotation difficulties discussed in section UID86   The next example in Figure FIGREF146 (b) shows that even if boundaries of components were detected precisely, the distinction between premise and backing fails  The example also shows that in some cases, labeling on clause level is required (left-hand side claim and premise) but the approximation in the system cannot cope with this level of detail (as explained in section UID111 )  Confusing non-argumentative text and argument components by the system is sometimes plausible, as is the case of the last rhetorical question in Figure FIGREF146 (c)  On the other hand, the last example in Figure FIGREF146 (d) shows that some claims using figurative language were hard to be identified  The complete predictions along with the gold data are publicly available 
SVMhmm offers many hyper-parameters with suggested default values, from which three are of importance  Parameter INLINEFORM0 sets the order of dependencies of transitions in HMM, parameter INLINEFORM1 sets the order of dependencies of emissions in HMM, and parameter INLINEFORM2 represents a trading-off slack versus magnitude of the weight-vector  For all experiments, we set all the hyper-parameters to their default values ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 )  Using the best performing feature set from Table TABREF139 , we experimented with a grid search over different values ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) but the results did not outperform the system trained with default parameter values 
The INLINEFORM0 scores might seem very low at the first glance  One obvious reason is the actual performance of the system, which gives a plenty of room for improvement in the future  But the main cause of low INLINEFORM2 numbers is the evaluation measure — using 11 classes on the token level is very strict, as it penalizes a mismatch in argument component boundaries the same way as a wrongly predicted argument component type  Therefore we also report two another evaluation metrics that help to put our results into a context 
Krippendorff's INLINEFORM0 — It was also used for evaluating inter-annotator agreement (see section UID75 ) 
Boundary similarity BIBREF125 — Using this metric, the problem is treated solely as a segmentation task without recognizing the argument component types 
As shown in Table TABREF157 (the Macro- INLINEFORM0 scores are repeated from Table TABREF139 ), the best-performing system achieves 0 30 score using Krippendorf's INLINEFORM1 , which is in the middle between the baseline and the human performance (0 48) but is considered as poor from the inter-annotator agreement point of view BIBREF54   The boundary similarity metrics is not directly suitable for evaluating argument component classification, but reveals a sub-task of finding the component boundaries  The best system achieved 0 32 on this measure  Vovk2013MT used this measure to annotate argument spans and his annotators achieved 0 36 boundary similarity score  Human annotators in BIBREF125 reached 0 53 boundary similarity score 
The overall performance of the system is also affected by the accuracy of individual NPL tools used for extracting features  One particular problem is that the preprocessing models we rely on (POS, syntax, semantic roles, coreference, discourse; see section UID115 ) were trained on newswire corpora, so one has to expect performance drop when applied on user-generated content  This is however a well-known issue in NLP BIBREF126 , BIBREF127 , BIBREF128  
To get an impression of the actual performance of the system on the data, we also provide the complete output of our best performing system in one PDF document together with the gold annotations in the logos dimension side by side in the accompanying software package  We believe this will help the community to see the strengths of our model as well as possible limitations of our current approaches 
Conclusions
Let us begin with summarizing answers to the research questions stated in the introduction  First, as we showed in section UID55 , existing argumentation theories do offer models for capturing argumentation in user-generated content on the Web  We built upon the Toulmin's model and proposed some extensions 
Second, as compared to the negative experiences with annotating using Walton's schemes (see sections UID52 and SECREF31 ), our modified Toulmin's model offers a trade-off between its expressiveness and annotation reliability  However, we found that the capabilities of the model to capture argumentation depend on the register and topic, the length of the document, and inherently on the literary devices and structures used for expressing argumentation as these properties influenced the agreement among annotators 
Third, there are aspects of online argumentation that lack their established theoretical counterparts, such as rhetorical questions, figurative language, narratives, and fallacies in general  We tried to model some of them in the pathos dimension of argument (section UID103 ), but no satisfying agreement was reached  Furthermore, we dealt with a step that precedes argument analysis by filtering documents given their persuasiveness with respect to the controversy  Finally, we proposed a computational model based on machine learning for identifying argument components (section SECREF108 )  In this identification task, we experimented with a wide range of linguistically motivated features and found that (1) the largest feature set (including n-grams, structural features, syntactic features, topic distribution, sentiment distribution, semantic features, coreference feaures, discourse features, and features based on word embeddings) performs best in both in-domain and all-data cross validation, while (2) features based only on word embeddings yield best results in cross-domain evaluation 
Since there is no one-size-fits-all argumentation theory to be applied to actual data on the Web, the argumentation model and an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties  Its selection should be based on the data at hand and the desired application  Given the proposed use-case scenarios (section SECREF1 ) and the results of our annotation study (section SECREF51 ), we recommend a scheme based on Toulmin's model for short documents, such as comments or forum posts 
Context 2: Introduction
There exists a class of language construction known as pun in natural language texts and utterances, where a certain word or other lexical items are used to exploit two or more separate meanings  It has been shown that understanding of puns is an important research question with various real-world applications, such as human-computer interaction BIBREF0 , BIBREF1 and machine translation BIBREF2   Recently, many researchers show their interests in studying puns, like detecting pun sentences BIBREF3 , locating puns in the text BIBREF4 , interpreting pun sentences BIBREF5 and generating sentences containing puns BIBREF6 , BIBREF7 , BIBREF8   A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect  Puns can be generally categorized into two groups, namely heterographic puns (where the pun and its latent target are phonologically similar) and homographic puns (where the two meanings of the pun reflect its two distinct senses) BIBREF9   Consider the following two examples:
The first punning joke exploits the sound similarity between the word “propane" and the latent target “profane", which can be categorized into the group of heterographic puns  Another categorization of English puns is homographic pun, exemplified by the second instance leveraging distinct senses of the word “gut" 
Pun detection is the task of detecting whether there is a pun residing in the given text  The goal of pun location is to find the exact word appearing in the text that implies more than one meanings  Most previous work addresses such two tasks separately and develop separate systems BIBREF10 , BIBREF5   Typically, a system for pun detection is built to make a binary prediction on whether a sentence contains a pun or not, where all instances (with or without puns) are taken into account during training  For the task of pun location, a separate system is used to make a single prediction as to which word in the given sentence in the text that trigger more than one semantic interpretations of the text, where the training data involves only sentences that contain a pun  Therefore, if one is interested in solving both problems at the same time, a pipeline approach that performs pun detection followed by pun location can be used 
Compared to the pipeline methods, joint learning has been shown effective BIBREF11 , BIBREF12 since it is able to reduce error propagation and allows information exchange between tasks which is potentially beneficial to all the tasks  In this work, we demonstrate that the detection and location of puns can be jointly addressed by a single model  The pun detection and location tasks can be combined as a sequence labeling problem, which allows us to jointly detect and locate a pun in a sentence by assigning each word a tag  Since each context contains a maximum of one pun BIBREF9 , we design a novel tagging scheme to capture this structural constraint  Statistics on the corpora also show that a pun tends to appear in the second half of a context  To capture such a structural property, we also incorporate word position knowledge into our structured prediction model  Experiments on the benchmark datasets show that detection and location tasks can reinforce each other, leading to new state-of-the-art performance on these two tasks  To the best of our knowledge, this is the first work that performs joint detection and location of English puns by using a sequence labeling approach 
Problem Definition
We first design a simple tagging scheme consisting of two tags { INLINEFORM0 }:
INLINEFORM0 tag means the current word is not a pun 
INLINEFORM0 tag means the current word is a pun 
If the tag sequence of a sentence contains a INLINEFORM0 tag, then the text contains a pun and the word corresponding to INLINEFORM1 is the pun 
The contexts have the characteristic that each context contains a maximum of one pun BIBREF9   In other words, there exists only one pun if the given sentence is detected as the one containing a pun  Otherwise, there is no pun residing in the text  To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 } 
INLINEFORM0 tag indicates that the current word appears before the pun in the given context 
INLINEFORM0 tag highlights the current word is a pun 
INLINEFORM0 tag indicates that the current word appears after the pun 
We empirically show that the INLINEFORM0 scheme can guarantee the context property that there exists a maximum of one pun residing in the text 
Given a context from the training set, we will be able to generate its corresponding gold tag sequence using a deterministic procedure  Under the two schemes, if a sentence does not contain any puns, all words will be tagged with INLINEFORM0 or INLINEFORM1 , respectively  Exemplified by the second sentence “Some diets cause a gut reaction," the pun is given as “gut " Thus, under the INLINEFORM2 scheme, it should be tagged with INLINEFORM3 , while the words before it are assigned with the tag INLINEFORM4 and words after it are with INLINEFORM5 , as illustrated in Figure FIGREF8   Likewise, the INLINEFORM6 scheme tags the word “gut" with INLINEFORM7 , while other words are tagged with INLINEFORM8   Therefore, we can combine the pun detection and location tasks into one problem which can be solved by the sequence labeling approach 
Model
Neural models have shown their effectiveness on sequence labeling tasks BIBREF13 , BIBREF14 , BIBREF15   In this work, we adopt the bidirectional Long Short Term Memory (BiLSTM) BIBREF16 networks on top of the Conditional Random Fields BIBREF17 (CRF) architecture to make labeling decisions, which is one of the classical models for sequence labeling  Our model architecture is illustrated in Figure FIGREF8 with a running example  Given a context/sentence INLINEFORM0 where INLINEFORM1 is the length of the context, we generate the corresponding tag sequence INLINEFORM2 based on our designed tagging schemes and the original annotations for pun detection and location provided by the corpora  Our model is then trained on pairs of INLINEFORM3  
Input  The contexts in the pun corpus hold the property that each pun contains exactly one content word, which can be either a noun, a verb, an adjective, or an adverb  To capture this characteristic, we consider lexical features at the character level  Similar to the work of BIBREF15 , the character embeddings are trained by the character-level LSTM networks on the unannotated input sequences  Nonlinear transformations are then applied to the character embeddings by highway networks BIBREF18 , which map the character-level features into different semantic spaces 
We also observe that a pun tends to appear at the end of a sentence  Specifically, based on the statistics, we found that sentences with a pun that locate at the second half of the text account for around 88% and 92% in homographic and heterographic datasets, respectively  We thus introduce a binary feature that indicates if a word is located at the first or the second half of an input sentence to capture such positional information  A binary indicator can be mapped to a vector representation using a randomly initialized embedding table BIBREF19 , BIBREF20   In this work, we directly adopt the value of the binary indicator as part of the input 
The concatenation of the transformed character embeddings, the pre-trained word embeddings BIBREF21 , and the position indicators are taken as input of our model 
Tagging  The input is then fed into a BiLSTM network, which will be able to capture contextual information  For a training instance INLINEFORM0 , we suppose the output by the word-level BiLSTM is INLINEFORM1   The CRF layer is adopted to capture label dependencies and make final tagging decisions at each position, which has been included in many state-of-the-art sequence labeling models BIBREF14 , BIBREF15   The conditional probability is defined as:
where INLINEFORM0 is a set of all possible label sequences consisting of tags from INLINEFORM1 (or INLINEFORM2 ), INLINEFORM3 and INLINEFORM4 are weight and bias parameters corresponding to the label pair INLINEFORM5   During training, we minimize the negative log-likelihood summed over all training instances:
where INLINEFORM0 refers to the INLINEFORM1 -th instance in the training set  During testing, we aim to find the optimal label sequence for a new input INLINEFORM2 :
This search process can be done efficiently using the Viterbi algorithm 
Datasets and Settings
We evaluate our model on two benchmark datasets BIBREF9   The homographic dataset contains 2,250 contexts, 1,607 of which contain a pun  The heterographic dataset consists of 1,780 contexts with 1,271 containing a pun  We notice there is no standard splitting information provided for both datasets  Thus we apply 10-fold cross validation  To make direct comparisons with prior studies, following BIBREF4 , we accumulated the predictions for all ten folds and calculate the scores in the end 
For each fold, we randomly select 10% of the instances from the training set for development  Word embeddings are initialized with the 100-dimensional Glove BIBREF21   The dimension of character embeddings is 30 and they are randomly initialized, which can be fine tuned during training  The pre-trained word embeddings are not updated during training  The dimensions of hidden vectors for both char-level and word-level LSTM units are set to 300  We adopt stochastic gradient descent (SGD) BIBREF26 with a learning rate of 0

ground_truth: claim, premise, backing, rebuttal, and refutation

claim, premise, backing, rebuttal, refutation

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What baselines do they compare with?
Context 2: 015 
For the pun detection task, if the predicted tag sequence contains at least one INLINEFORM0 tag, we regard the output (i e , the prediction of our pun detection model) for this task as true, otherwise false  For the pun location task, a predicted pun is regarded as correct if and only if it is labeled as the gold pun in the dataset  As to pun location, to make fair comparisons with prior studies, we only consider the instances that are labeled as the ones containing a pun  We report precision, recall and INLINEFORM1 score in Table TABREF11   A list of prior works that did not employ joint learning are also shown in the first block of Table TABREF11  
Results
We also implemented a baseline model based on conditional random fields (CRF), where features like POS tags produced by the Stanford POS tagger BIBREF27 , n-grams, label transitions, word suffixes and relative position to the end of the text are considered  We can see that our model with the INLINEFORM0 tagging scheme yields new state-of-the-art INLINEFORM1 scores on pun detection and competitive results on pun location, compared to baselines that do not adopt joint learning in the first block  For location on heterographic puns, our model's performance is slightly lower than the system of BIBREF25 , which is a rule-based locator  Compared to CRF, we can see that our model, either with the INLINEFORM2 or the INLINEFORM3 scheme, yields significantly higher recall on both detection and location tasks, while the precisions are relatively close  This demonstrates the effectiveness of BiLSTM, which learns the contextual features of given texts – such information appears to be helpful in recalling more puns 
Compared to the INLINEFORM0 scheme, the INLINEFORM1 tagging scheme is able to yield better performance on these two tasks  After studying outputs from these two approaches, we found that one leading source of error for the INLINEFORM2 approach is that there exist more than one words in a single instance that are assigned with the INLINEFORM3 tag  However, according to the description of pun in BIBREF9 , each context contains a maximum of one pun  Thus, such a useful structural constraint is not well captured by the simple approach based on the INLINEFORM4 tagging scheme  On the other hand, by applying the INLINEFORM5 tagging scheme, such a constraint is properly captured in the model  As a result, the results for such a approach are significantly better than the approach based on the INLINEFORM6 tagging scheme, as we can observe from the table  Under the same experimental setup, we also attempted to exclude word position features  Results are given by INLINEFORM7 - INLINEFORM8   It is expected that the performance of pun location drops, since such position features are able to capture the interesting property that a pun tends to appear in the second half of a sentence  While such knowledge is helpful for the location task, interestingly, a model without position knowledge yields improved performance on the pun detection task  One possible reason is that detecting whether a sentence contains a pun is not concerned with such word position information 
Additionally, we conduct experiments over sentences containing a pun only, namely 1,607 and 1,271 instances from homographic and heterographic pun corpora separately  It can be regarded as a “pipeline” method where the classifier for pun detection is regarded as perfect  Following the prior work of BIBREF4 , we apply 10-fold cross validation  Since we are given that all input sentences contain a pun, we only report accumulated results on pun location, denoted as Pipeline in Table TABREF11   Compared with our approaches, the performance of such an approach drops significantly  On the other hand, such a fact demonstrates that the two task, detection and location of puns, can reinforce each other  These figures demonstrate the effectiveness of our sequence labeling method to detect and locate English puns in a joint manner 
Error Analysis
We studied the outputs from our system and make some error analysis  We found the errors can be broadly categorized into several types, and we elaborate them here  1) Low word coverage: since the corpora are relatively small, there exist many unseen words in the test set  Learning the representations of such unseen words is challenging, which affects the model's performance  Such errors contribute around 40% of the total errors made by our system  2) Detection errors: we found many errors are due to the model's inability to make correct pun detection  Such inability harms both pun detection and pun location  Although our approach based on the INLINEFORM0 tagging scheme yields relatively higher scores on the detection task, we still found that 40% of the incorrectly predicted instances fall into this group  3) Short sentences: we found it was challenging for our model to make correct predictions when the given text is short  Consider the example “Superglue! Tom rejoined," here the word rejoined is the corresponding pun  However, it would be challenging to figure out the pun with such limited contextual information 
Related Work
Most existing systems address pun detection and location separately  BIBREF22 applied word sense knowledge to conduct pun detection  BIBREF24 trained a bidirectional RNN classifier for detecting homographic puns  Next, a knowledge-based approach is adopted to find the exact pun  Such a system is not applicable to heterographic puns  BIBREF28 applied Google n-gram and word2vec to make decisions  The phonetic distance via the CMU Pronouncing Dictionary is computed to detect heterographic puns  BIBREF10 used the hidden Markov model and a cyclic dependency network with rich features to detect and locate puns  BIBREF23 used a supervised approach to pun detection and a weakly supervised approach to pun location based on the position within the context and part of speech features  BIBREF25 proposed a rule-based system for pun location that scores candidate words according to eleven simple heuristics  Two systems are developed to conduct detection and location separately in the system known as UWAV BIBREF3   The pun detector combines predictions from three classifiers  The pun locator considers word2vec similarity between every pair of words in the context and position to pinpoint the pun  The state-of-the-art system for homographic pun location is a neural method BIBREF4 , where the word senses are incorporated into a bidirectional LSTM model  This method only supports the pun location task on homographic puns  Another line of research efforts related to this work is sequence labeling, such as POS tagging, chunking, word segmentation and NER  The neural methods have shown their effectiveness in this task, such as BiLSTM-CNN BIBREF13 , GRNN BIBREF29 , LSTM-CRF BIBREF30 , LSTM-CNN-CRF BIBREF14 , LM-LSTM-CRF BIBREF15  
In this work, we combine pun detection and location tasks as a single sequence labeling problem  Inspired by the work of BIBREF15 , we also adopt a LSTM-CRF with character embeddings to make labeling decisions 
Conclusion
In this paper, we propose to perform pun detection and location tasks in a joint manner from a sequence labeling perspective  We observe that each text in our corpora contains a maximum of one pun  Hence, we design a novel tagging scheme to incorporate such a constraint  Such a scheme guarantees that there is a maximum of one word that will be tagged as a pun during the testing phase  We also found the interesting structural property such as the fact that most puns tend to appear at the second half of the sentences can be helpful for such a task, but was not explored in previous works  Furthermore, unlike many previous approaches, our approach, though simple, is generally applicable to both heterographic and homographic puns  Empirical results on the benchmark datasets prove the effectiveness of the proposed approach that the two tasks of pun detection and location can be addressed by a single model from a sequence labeling perspective 
Future research includes the investigations on how to make use of richer semantic and linguistic information for detection and location of puns  Research on puns for other languages such as Chinese is still under-explored, which could also be an interesting direction for our future studies 
Acknowledgments
We would like to thank the three anonymous reviewers for their thoughtful and constructive comments  This work is supported by Singapore Ministry of Education Academic Research Fund (AcRF) Tier 2 Project MOE2017-T2-1-156, and is partially supported by SUTD project PIE-SGP-AI-2018-01 

ground_truth: They compare with the following models: by Pedersen (2017), by Pramanick and Das (2017), by Mikhalkova and Karyakin (2017),  by Vadehra (2017), Indurthi and Oota (2017), by Vechtomova (2017), by (Cai et al., 2018), and CRF.

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are the source and target domains?
Context 3: Introduction
In practice, it is often difficult and costly to annotate sufficient training data for diverse application domains on-the-fly  We may have sufficient labeled data in an existing domain (called the source domain), but very few or no labeled data in a new domain (called the target domain)  This issue has motivated research on cross-domain sentiment classification, where knowledge in the source domain is transferred to the target domain in order to alleviate the required labeling effort 
One key challenge of domain adaptation is that data in the source and target domains are drawn from different distributions  Thus, adaptation performance will decline with an increase in distribution difference  Specifically, in sentiment analysis, reviews of different products have different vocabulary  For instance, restaurants reviews would contain opinion words such as “tender”, “tasty”, or “undercooked” and movie reviews would contain “thrilling”, “horrific”, or “hilarious”  The intersection between these two sets of opinion words could be small which makes domain adaptation difficult 
Several techniques have been proposed for addressing the problem of domain shifting  The aim is to bridge the source and target domains by learning domain-invariant feature representations so that a classifier trained on a source domain can be adapted to another target domain  In cross-domain sentiment classification, many works BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 utilize a key intuition that domain-specific features could be aligned with the help of domain-invariant features (pivot features)  For instance, “hilarious” and “tasty” could be aligned as both of them are relevant to “good” 
Despite their promising results, these works share two major limitations  First, they highly depend on the heuristic selection of pivot features, which may be sensitive to different applications  Thus the learned new representations may not effectively reduce the domain difference  Furthermore, these works only utilize the unlabeled target data for representation learning while the sentiment classifier was solely trained on the source domain  There have not been many studies on exploiting unlabeled target data for refining the classifier, even though it may contain beneficial information  How to effectively leverage unlabeled target data still remains an important challenge for domain adaptation 
In this work, we argue that the information from unlabeled target data is beneficial for domain adaptation and we propose a novel Domain Adaptive Semi-supervised learning framework (DAS) to better exploit it  Our main intuition is to treat the problem as a semi-supervised learning task by considering target instances as unlabeled data, assuming the domain distance can be effectively reduced through domain-invariant representation learning  Specifically, the proposed approach jointly performs feature adaptation and semi-supervised learning in a multi-task learning setting  For feature adaptation, it explicitly minimizes the distance between the encoded representations of the two domains  On this basis, two semi-supervised regularizations – entropy minimization and self-ensemble bootstrapping – are jointly employed to exploit unlabeled target data for classifier refinement 
We evaluate our method rigorously under multiple experimental settings by taking label distribution and corpus size into consideration  The results show that our model is able to obtain significant improvements over strong baselines  We also demonstrate through a series of analysis that the proposed method benefits greatly from incorporating unlabeled target data via semi-supervised learning, which is consistent with our motivation  Our datasets and source code can be obtained from https://github com/ruidan/DAS 
Related Work
Domain Adaptation: The majority of feature adaptation methods for sentiment analysis rely on a key intuition that even though certain opinion words are completely distinct for each domain, they can be aligned if they have high correlation with some domain-invariant opinion words (pivot words) such as “excellent” or “terrible”  Blitzer et al  ( BIBREF0 ) proposed a method based on structural correspondence learning (SCL), which uses pivot feature prediction to induce a projected feature space that works well for both the source and the target domains  The pivot words are selected in a way to cover common domain-invariant opinion words  Subsequent research aims to better align the domain-specific words BIBREF1 , BIBREF5 , BIBREF3 such that the domain discrepancy could be reduced  More recently, Yu and Jiang ( BIBREF4 ) borrow the idea of pivot feature prediction from SCL and extend it to a neural network-based solution with auxiliary tasks  In their experiment, substantial improvement over SCL has been observed due to the use of real-valued word embeddings  Unsupervised representation learning with deep neural networks (DNN) such as denoising autoencoders has also been explored for feature adaptation BIBREF6 , BIBREF7 , BIBREF8   It has been shown that DNNs could learn transferable representations that disentangle the underlying factors of variation behind data samples 
Although the aforementioned methods aim to reduce the domain discrepancy, they do not explicitly minimize the distance between distributions, and some of them highly rely on the selection of pivot features  In our method, we formally construct an objective for this purpose  Similar ideas have been explored in many computer vision problems, where the representations of the underlying domains are encouraged to be similar through explicit objectives BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 such as maximum mean discrepancy (MMD) BIBREF14   In NLP tasks, Li et al  ( BIBREF15 ) and Chen et al  ( BIBREF16 ) both proposed using adversarial training framework for reducing domain difference  In their model, a sub-network is added as a domain discriminator while deep features are learned to confuse the discriminator  The feature adaptation component in our model shares similar intuition with MMD and adversary training  We will show a detailed comparison with them in our experiments 
Semi-supervised Learning: We attempt to treat domain adaptation as a semi-supervised learning task by considering the target instances as unlabeled data  Some efforts have been initiated on transfer learning from unlabeled data BIBREF17 , BIBREF18 , BIBREF19   In our model, we reduce the domain discrepancy by feature adaptation, and thereafter adopt semi-supervised learning techniques to learn from unlabeled data  Primarily motivated by BIBREF20 and BIBREF21 , we employed entropy minimization and self-ensemble bootstrapping as regularizations to incorporate unlabeled data  Our experimental results show that both methods are effective when jointly trained with the feature adaptation objective, which confirms to our motivation 
Notations and Model Overview
We conduct most of our experiments under an unsupervised domain adaptation setting, where we have no labeled data from the target domain  Consider two sets INLINEFORM0 and INLINEFORM1   INLINEFORM2 is from the source domain with INLINEFORM3 labeled examples, where INLINEFORM4 is a one-hot vector representation of sentiment label and INLINEFORM5 denotes the number of classes  INLINEFORM6 is from the target domain with INLINEFORM7 unlabeled examples  INLINEFORM8 denotes the total number of training documents including both labeled and unlabeled  We aim to learn a sentiment classifier from INLINEFORM13 and INLINEFORM14 such that the classifier would work well on the target domain  We also present some results under a setting where we assume that a small number of labeled target examples are available (see Figure FIGREF27 ) 
For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier  We aim to learn feature representations that are domain-invariant and at the same time discriminative on both domains, thus we simultaneously consider three factors in our objective: (1) minimize the classification error on the labeled source examples; (2) minimize the domain discrepancy; and (3) leverage unlabeled data via semi-supervised learning 
Suppose we already have the encoded features of documents INLINEFORM0 (see Section SECREF10 ), the objective function for purpose (1) is thus the cross entropy loss on the labeled source examples DISPLAYFORM0
where INLINEFORM0 denotes the predicted label distribution  In the following subsections, we will explain how to perform feature adaptation and domain adaptive semi-supervised learning in details for purpose (2) and (3) respectively 
Feature Adaptation
Unlike prior works BIBREF0 , BIBREF4 , our method does not attempt to align domain-specific words through pivot words  In our preliminary experiments, we found that word embeddings pre-trained on a large corpus are able to adequately capture this information  As we will later show in our experiments, even without adaptation, a naive neural network classifier with pre-trained word embeddings can already achieve reasonably good results 
We attempt to explicitly minimize the distance between the source and target feature representations ( INLINEFORM0 and INLINEFORM1 )  A few methods from literature can be applied such as Maximum Mean Discrepancy (MMD) BIBREF14 or adversary training BIBREF15 , BIBREF16   The main idea of MMD is to estimate the distance between two distributions as the distance between sample means of the projected embeddings in Hilbert space  MMD is implicitly computed through a characteristic kernel, which is used to ensure that the sample mean is injective, leading to the MMD being zero if and only if the distributions are identical  In our implementation, we skip the mapping procedure induced by a characteristic kernel for simplifying the computation and learning  We simply estimate the distribution distance as the distance between the sample means in the current embedding space  Although this approximation cannot preserve all statistical features of the underlying distributions, we find it performs comparably to MMD on our problem  The following equations formally describe the feature adaptation loss INLINEFORM2 : DISPLAYFORM0
INLINEFORM0 normalization is applied on the mean representations INLINEFORM1 and INLINEFORM2 , rescaling the vectors such that all entries sum to 1  We adopt a symmetric version of KL divergence BIBREF12 as the distance function  Given two distribution vectors INLINEFORM3 , INLINEFORM4  
Domain Adaptive Semi-supervised Learning (DAS)
We attempt to exploit the information in target data through semi-supervised learning objectives, which are jointly trained with INLINEFORM0 and INLINEFORM1   Normally, to incorporate target data, we can minimize the cross entropy loss between the true label distributions INLINEFORM2 and the predicted label distributions INLINEFORM3 over target samples  The challenge here is that INLINEFORM4 is unknown, and thus we attempt to estimate it via semi-supervised learning  We use entropy minimization and bootstrapping for this purpose  We will later show in our experiments that both methods are effective, and jointly employing them overall yields the best results 
Entropy Minimization: In this method, INLINEFORM0 is estimated as the predicted label distribution INLINEFORM1 , which is a function of INLINEFORM2 and INLINEFORM3   The loss can thus be written as DISPLAYFORM0
Assume the domain discrepancy can be effectively reduced through feature adaptation, by minimizing the entropy penalty, training of the classifier is influenced by the unlabeled target data and will generally maximize the margins between the target examples and the decision boundaries, increasing the prediction confidence on the target domain 
Self-ensemble Bootstrapping: Another way to estimate INLINEFORM0 corresponds to bootstrapping  The idea is to estimate the unknown labels as the predictions of the model learned from the previous round of training  Bootstrapping has been explored for domain adaptation in previous works BIBREF18 , BIBREF19   However, in their methods, domain discrepancy was not explicitly minimized via feature adaptation  Applying bootstrapping or other semi-supervised learning techniques in this case may worsen the results as the classifier can perform quite bad on the target data 
[t] Pseudocode for training DAS INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 INLINEFORM4 = ensembling momentum, INLINEFORM5 INLINEFORM6 = weight ramp-up function INLINEFORM7 INLINEFORM8 INLINEFORM9 each minibatch INLINEFORM10 , INLINEFORM11 , INLINEFORM12 in
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 compute loss INLINEFORM3 on INLINEFORM4 compute loss INLINEFORM5 on INLINEFORM6 compute loss INLINEFORM7 on INLINEFORM8 compute loss INLINEFORM9 on INLINEFORM10 INLINEFORM11
update network parameters INLINEFORM0 , for INLINEFORM1 INLINEFORM2 INLINEFORM3
Inspired by the ensembling method proposed in BIBREF21 , we estimate INLINEFORM0 by forming ensemble predictions of labels during training, using the outputs on different training epochs  The loss is formulated as follows: DISPLAYFORM0
where INLINEFORM0 denotes the estimated labels computed on the ensemble predictions from different epochs  The loss is applied on all documents  It serves for bootstrapping on the unlabeled target data, and it also serves as a regularization that encourages the network predictions to be consistent in different training epochs  INLINEFORM1 is jointly trained with INLINEFORM2 , INLINEFORM3 , and INLINEFORM4   Algorithm SECREF6 illustrates the overall training process of the proposed domain adaptive semi-supervised learning (DAS) framework 
In Algorithm SECREF6 , INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are weights to balance the effects of INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 respectively  INLINEFORM6 and INLINEFORM7 are constant hyper-parameters  We set INLINEFORM8 as a Gaussian curve to ramp up the weight from 0 to INLINEFORM9   This is to ensure the ramp-up of the bootstrapping loss component is slow enough in the beginning of the training  After each training epoch, we compute INLINEFORM10 which denotes the predictions made by the network in current epoch, and then the ensemble prediction INLINEFORM11 is updated as a weighted average of the outputs from previous epochs and the current epoch, with recent epochs having larger weight  For generating estimated labels INLINEFORM12 , INLINEFORM13 is converted to a one-hot vector where the entry with the maximum value is set to one and other entries are set to zeros  The self-ensemble bootstrapping is a generalized version of bootstrappings that only use the outputs from the previous round of training BIBREF18 , BIBREF19   The ensemble prediction is likely to be closer to the correct, unknown labels of the target data 
CNN Encoder Implementation
We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered  In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks  Given a review document INLINEFORM1 consisting of INLINEFORM2 words, we begin by associating each word with a continuous word embedding BIBREF23 INLINEFORM3 from an embedding matrix INLINEFORM4 , where INLINEFORM5 is the vocabulary size and INLINEFORM6 is the embedding dimension  INLINEFORM7 is jointly updated with other network parameters during training  Given a window of dense word embeddings INLINEFORM8 , the convolution layer first concatenates these vectors to form a vector INLINEFORM9 of length INLINEFORM10 and then the output vector is computed by Equation ( EQREF11 ): DISPLAYFORM0
INLINEFORM0 , INLINEFORM1 is the parameter set of the encoder INLINEFORM2 and is shared across all windows of the sequence  INLINEFORM3 is an element-wise non-linear activation function  The convolution operation can capture local contextual dependencies of the input sequence and the extracted feature vectors are similar to INLINEFORM4 -grams  After the convolution operation is applied to the whole sequence, we obtain a list of hidden vectors INLINEFORM5   A max-over-time pooling layer is applied to obtain the final vector representation INLINEFORM6 of the input document 
Datasets and Experimental Settings
Existing benchmark datasets such as the Amazon benchmark BIBREF0 typically remove reviews with neutral labels in both domains  This is problematic as the label information of the target domain is not accessible in an unsupervised domain adaptation setting  Furthermore, removing neutral instances may bias the dataset favorably for max-margin-based algorithms like ours, since the resulting dataset has all uncertain labels removed, leaving only high confidence examples  Therefore, we construct new datasets by ourselves  The results on the original Amazon benchmark is qualitatively similar, and we present them in Appendix SECREF6 for completeness since most of previous works reported results on it 
Small-scale datasets: Our new dataset was derived from the large-scale Amazon datasets released by McAuley et al  ( BIBREF24 )  It contains four domains: Book (BK), Electronics (E), Beauty (BT), and Music (M)  Each domain contains two datasets  Set 1 contains 6000 instances with exactly balanced class labels, and set 2 contains 6000 instances that are randomly sampled from the large dataset, preserving the original label distribution, which we believe better reflects the label distribution in real life  The examples in these two sets do not overlap  Detailed statistics of the generated datasets are given in Table TABREF9  
In all our experiments on the small-scale datasets, we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain  Since we cannot control the label distribution of unlabeled data during training, we consider two different settings:
Setting (1): Only set 1 of the target domain is used as the unlabeled set  This tells us how the method performs in a condition when the target domain has a close-to-balanced label distribution  As we also evaluate on set 1 of the target domain, this is also considered as a transductive setting 
Setting (2): Set 2 from both the source and target domains are used as unlabeled sets  Since set 2 is directly sampled from millions of reviews, it better reflects real-life sentiment distribution 
Large-scale datasets: We further conduct experiments on four much larger datasets: IMDB (I), Yelp2014 (Y), Cell Phone (C), and Baby (B)  IMDB and Yelp2014 were previously used in BIBREF25 , BIBREF26   Cell phone and Baby are from the large-scale Amazon dataset BIBREF24 , BIBREF27   Detailed statistics are summarized in Table TABREF9   We keep all reviews in the original datasets and consider a transductive setting where all target examples are used for both training (without label information) and evaluation  We perform sampling to balance the classes of labeled source data in each minibatch INLINEFORM3 during training 
Selection of Development Set
Ideally, the development set should be drawn from the same distribution as the test set  However, under the unsupervised domain adaptation setting, we do not have any labeled target data at training phase which could be used as development set  In all of our experiments, for each pair of domains, we instead sample 1000 examples from the training set of the source domain as development set  We train the network for a fixed number of epochs, and the model with the minimum classification error on this development set is saved for evaluation  This approach works well on most of the problems since the target domain is supposed to behave like the source domain if the domain difference is effectively reduced 
Another problem is how to select the values for hyper-parameters  If we tune INLINEFORM0 and INLINEFORM1 directly on the development set from the source domain, most likely both of them will be set to 0, as unlabeled target data is not helpful for improving in-domain accuracy of the source domain  Other neural network models also have the same problem for hyper-parameter tuning  Therefore, our strategy is to use the development set from the target domain to optimize INLINEFORM2 and INLINEFORM3 for one problem (e g , we only do this on E INLINEFORM4 BK), and fix their values on the other problems  This setting assumes that we have at least two labeled domains such that we can optimize the hyper-parameters, and then we fix them for other new unlabeled domains to transfer to 
Training Details and Hyper-parameters
We initialize word embeddings using the 300-dimension GloVe vectors supplied by Pennington et al , ( BIBREF28 ), which were trained on 840 billion tokens from the Common Crawl  For each pair of domains, the vocabulary consists of the top 10000 most frequent words  For words in the vocabulary but not present in the pre-trained embeddings, we randomly initialize them 
We set hyper-parameters of the CNN encoder following previous works BIBREF22 , BIBREF4 without specific tuning on our datasets  The window size is set to 3 and the size of the hidden layer is set to 300  The nonlinear activation function is Relu  For regularization, we also follow their settings and employ dropout with probability set to 0 5 on INLINEFORM0 before feeding it to the output layer INLINEFORM1 , and constrain the INLINEFORM2 -norm of the weight vector INLINEFORM3 , setting its max norm to 3 
On the small-scale datasets and the Aamzon benchmark, INLINEFORM0 and INLINEFORM1 are set to 200 and 1, respectively, tuned on the development set of task E INLINEFORM2 BK under setting 1  On the large-scale datasets, INLINEFORM3 and INLINEFORM4 are set to 500 and 0 2, respectively, tuned on I INLINEFORM5 Y  We use a Gaussian curve INLINEFORM6 to ramp up the weight of the bootstrapping loss INLINEFORM7 from 0 to INLINEFORM8 , where INLINEFORM9 denotes the maximum number of training epochs  We train 30 epochs for all experiments  We set INLINEFORM10 to 3 and INLINEFORM11 to 0 5 for all experiments 
The batch size is set to 50 on the small-scale datasets and the Amazon benchmark  We increase the batch size to 250 on the large-scale datasets to reduce the number of iterations  RMSProp optimizer with learning rate set to 0 0005 is used for all experiments 
Models for Comparison
We compare with the following baselines:
(1) Naive: A non-domain-adaptive baseline with bag-of-words representations and SVM classifier trained on the source domain 
(2) mSDA BIBREF7 : This is the state-of-the-art method based on discrete input features  Top 1000 bag-of-words features are kept as pivot features  We set the number of stacked layers to 3 and the corruption probability to 0 5 
(3) NaiveNN: This is a non-domain-adaptive CNN trained on source domain, which is a variant of our model by setting INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 to zeros 
(4) AuxNN BIBREF4 : This is a neural model that exploits auxiliary tasks, which has achieved state-of-the-art results on cross-domain sentiment classification  The sentence encoder used in this model is the same as ours 
(5) ADAN BIBREF16 : This method exploits adversarial training to reduce representation difference between domains  The original paper uses a simple feedforward network as encoder  For fair comparison, we replace it with our CNN-based encoder  We train 5 iterations on the discriminator per iteration on the encoder and sentiment classifier as suggested in their paper 
(6) MMD: MMD has been widely used for minimizing domain discrepancy on images  In those works BIBREF9 , BIBREF13 , variants of deep CNNs are used for encoding images and the MMDs of multiple layers are jointly minimized  In NLP, adding more layers of CNNs may not be very helpful and thus those models from image-related tasks can not be directly applied to our problem  To compare with MMD-based method, we train a model that jointly minimize the classification loss INLINEFORM0 on the source domain and MMD between INLINEFORM1 and INLINEFORM2   For computing MMD, we use a Gaussian RBF which is a common choice for characteristic kernel 
In addition to the above baselines, we also show results of different variants of our model  DAS as shown in Algorithm SECREF6 denotes our full model  DAS-EM denotes the model with only entropy minimization for semi-supervised learning (set INLINEFORM0 )  DAS-SE denotes the model with only self-ensemble bootstrapping for semi-supervised learning (set INLINEFORM1 )  FANN (feature-adaptation neural network) denotes the model without semi-supervised learning performed (set both INLINEFORM2 and INLINEFORM3 to zeros) 
Main Results
Figure FIGREF17 shows the comparison of adaptation results (see Appendix SECREF7 for the exact numerical numbers)  We report classification accuracy on the small-scale dataset  For the large-scale dataset, macro-F1 is instead used since the label distribution in the test set is extremely unbalanced  Key observations are summarized as follows  (1) Both DAS-EM and DAS-SE perform better in most cases compared with ADAN, MDD, and FANN, in which only feature adaptation is performed  This demonstrates the effectiveness of the proposed domain adaptive semi-supervised learning framework  DAS-EM is more effective than DAS-SE in most cases, and the full model DAS with both techniques jointly employed overall has the best performance  (2) When comparing the two settings on the small-scale dataset, all domain-adaptive methods generally perform better under setting 1  In setting 1, the target examples are balanced in classes, which can provide more diverse opinion-related features  However, when considering unsupervised domain adaptation, we should not presume the label distribution of the unlabeled data  Thus, it is necessary to conduct experiments using datasets that reflect real-life sentiment distribution as what we did on setting2 and the large-scale dataset  Unfortunately, this is ignored by most of previous works  (3) Word-embeddings are very helpful, as we can see even NaiveNN can substantially outperform mSDA on most tasks 
To see the effect of semi-supervised learning alone, we also conduct experiments by setting INLINEFORM0 to eliminate the effect of feature adaptation  Both entropy minimization and bootstrapping perform very badly in this setting  Entropy minimization gives almost random predictions with accuracy below 0 4, and the results of bootstrapping are also much lower compared to NaiveNN  This suggests that the feature adaptation component is essential  Without it, the learned target representations are less meaningful and discriminative  Applying semi-supervised learning in this case is likely to worsen the results 
Further Analysis
In Figure FIGREF23 , we show the change of accuracy with respect to the percentage of unlabeled data used for training on three particular problems under setting 1  The value at INLINEFORM0 denotes the accuracies of NaiveNN which does not utilize any target data  For DAS, we observe a nonlinear increasing trend where the accuracy quickly improves at the beginning, and then gradually stabilizes  For other methods, this trend is less obvious, and adding more unlabeled data sometimes even worsen the results  This finding again suggests that the proposed approach can better exploit the information from unlabeled data 
We also conduct experiments under a setting with a small number of labeled target examples available  Figure FIGREF27 shows the change of accuracy with respect to the number of labeled target examples added for training  We can observe that DAS is still more effective under this setting, while the performance differences to other methods gradually decrease with the increasing number of labeled target examples 
CNN Filter Analysis
In this subsection, we aim to better understand DAS by analyzing sentiment-related CNN filters  To do that, 1) we first select a list of the most related CNN filters for predicting each sentiment label (positive, negative neutral)  Those filters can be identified according to the learned weights INLINEFORM0 of the output layer INLINEFORM1   Higher weight indicates stronger relatedness  2) Recall that in our implementation, each CNN filter has a window size of 3 with Relu activation  We can thus represent each selected filter as a ranked list of trigrams with highest activation values 
We analyze the CNN filters learned by NaiveNN, FANN and DAS respectively on task E INLINEFORM0 BT under setting 1  We focus on E INLINEFORM1 BT for study because electronics and beauty are very different domains and each of them has a diverse set of domain-specific sentiment expressions  For each method, we identify the top 10 most related filters for each sentiment label, and extract the top trigrams of each selected filter on both source and target domains  Since labeled source examples are used for training, we find the filters learned by the three methods capture similar expressions on the source domain, containing both domain-invariant and domain-specific trigrams  On the target domain, DAS captures more target-specific expressions compared to the other two methods  Due to space limitation, we only present a small subset of positive-sentiment-related filters in Table TABREF34   The complete results are provided in Appendix SECREF8   From Table TABREF34 , we can observe that the filters learned by NaiveNN are almost unable to capture target-specific sentiment expressions, while FANN is able to capture limited target-specific words such as “clean” and “scent”  The filters learned by DAS are more domain-adaptive, capturing diverse sentiment expressions in the target domain 
Conclusion
In this work, we propose DAS, a novel framework that jointly performs feature adaptation and semi-supervised learning  We have demonstrated through multiple experiments that DAS can better leverage unlabeled data, and achieve substantial improvements over baseline methods  We have also shown that feature adaptation is an essential component, without which, semi-supervised learning is not able to function properly  The proposed framework could be potentially adapted to other domain adaptation tasks, which is the focus of our future studies 
Results on Amazon Benchmark
Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al  ( BIBREF0 )  The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K)  Following their experimental settings, we consider the binary classification task to predict whether a review is positive or negative on the target domain  Each domain consists of 1000 positive and 1000 negative reviews respectively  We also allow 4000 unlabeled reviews to be used for both the source and the target domains, of which the positive and negative reviews are balanced as well, following the settings in previous works  We construct 12 cross-domain sentiment classification tasks and split the labeled data in each domain into a training set of 1600 reviews and a test set of 400 reviews  The classifier is trained on the training set of the source domain and is evaluated on the test set of the target domain  The comparison results are shown in Table TABREF37  
Numerical Results of Figure 
Due to space limitation, we only show results in figures in the paper  All numerical numbers used for plotting Figure FIGREF17 are presented in Table TABREF38   We can observe that DAS-EM, DAS-SE, and DAS all achieve substantial improvements over baseline methods under different settings 
CNN Filter Analysis Full Results
As mentioned in Section SECREF36 , we conduct CNN filter analysis on NaiveNN, FANN, and DAS  For each method, we identify the top 10 most related filters for positive, negative, neutral sentiment labels respectively, and then represent each selected filter as a ranked list of trigrams with the highest activation values on it  Table TABREF39 , TABREF40 , TABREF41 in the following pages illustrate the trigrams from the target domain (beauty) captured by the selected filters learned on E INLINEFORM0 BT for each method 
We can observe that compared to NaiveNN and FANN, DAS is able to capture a more diverse set of relevant sentiment expressions on the target domain for each sentiment label  This observation is consistent with our motivation  Since NaiveNN, FANN and other baseline methods solely train the sentiment classifier on the source domain, the learned encoder is not able to produce discriminative features on the target domain  DAS addresses this problem by refining the classifier on the target domain with semi-supervised learning, and the overall objective forces the encoder to learn feature representations that are not only domain-invariant but also discriminative on both domains 

ground_truth: Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen

we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics (E), Beauty (BT), and Music (M)