pwc-archive/papers-with-abstracts huggingface dataset:
Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels
Authors: Felix Soldner, Justin Chun-ting Ho, Mykola Makhortykh, Isabelle W.J. van der Vegt, Maximilian Mozes, Bennett Kleinberg
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Clustering

News consumption exhibits an increasing shift towards online sources, which bring platforms such as YouTube more into focus. Thus, the distribution of politically loaded news is easier, receives more attention, but also raises the concern of forming isolated ideological communities. Understanding how such news is communicated and received is becoming increasingly important. To expand our understanding in this domain, we apply a linguistic temporal trajectory analysis to analyze sentiment patterns in English-language videos from news channels on YouTube. We examine transcripts from videos distributed through eight channels with pro-left and pro-right political leanings. Using unsupervised clustering, we identify seven different sentiment patterns in the transcripts. We found that the use of two sentiment patterns differed significantly depending on political leaning. Furthermore, we used predictive models to examine how different sentiment patterns relate to video popularity and if they differ depending on the channel{'}s political leaning. No clear relations between sentiment patterns and popularity were found. However, results indicate, that videos from pro-right news channels are more popular and that a negative sentiment further increases that popularity, when sentiments are averaged for each video.
Modeling Behavioral Aspects of Social Media Discourse for Moral Classification
Authors: Kristen Johnson, Dan Goldwasser
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Classification, General Classification

Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the length constraint of tweets, politicians must carefully word their statements to ensure their message is understood by their intended audience. This constraint often eliminates the context of the tweet, making automatic analysis of social media political discourse a difficult task. To overcome this challenge, we propose simultaneous modeling of high-level abstractions of political language, such as political slogans and framing strategies, with abstractions of how politicians behave on Twitter. These behavioral abstractions can be further leveraged as forms of supervision in order to increase prediction accuracy, while reducing the burden of annotation. In this work, we use Probabilistic Soft Logic (PSL) to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter. When combined, these descriptors reveal the moral foundations underlying the discourse of U.S. politicians online, \textit{across} differing governing administrations, showing how party talking points remain cohesive or change over time.
Scalable Methods for Annotating Legal-Decision Corpora
Authors: Lisa Ferro, John Aberdeen, Karl Branting, Craig Pfeifer, Alex Yeh, er, Amartya Chakraborty
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Recent research has demonstrated that judicial and administrative decisions can be predicted by machine-learning models trained on prior decisions. However, to have any practical application, these predictions must be explainable, which in turn requires modeling a rich set of features. Such approaches face a roadblock if the knowledge engineering required to create these features is not scalable. We present an approach to developing a feature-rich corpus of administrative rulings about domain name disputes, an approach which leverages a small amount of manual annotation and prototypical patterns present in the case documents to automatically extend feature labels to the entire corpus. To demonstrate the feasibility of this approach, we report results from systems trained on this dataset.
The Extent of Repetition in Contract Language
Authors: Dan Simonson, Daniel Broderick, Jonathan Herr
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Contract language is repetitive (Anderson and Manns, 2017), but so is all language (Zipf, 1949). In this paper, we measure the extent to which contract language in English is repetitive compared with the language of other English language corpora. Contracts have much smaller vocabulary sizes compared with similarly sized non-contract corpora across multiple contract types, contain 1/5th as many hapax legomena, pattern differently on a log-log plot, use fewer pronouns, and contain sentences that are about 20{\%} more similar to one another than in other corpora. These suggest that the study of contracts in natural language processing controls for some linguistic phenomena and allows for more in depth study of others.
Sentence Boundary Detection in Legal Text
Authors: George Sanchez
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Boundary Detection, Sentence

In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.
Legal Linking: Citation Resolution and Suggestion in Constitutional Law
Authors: Robert Shaffer, Stephen Mayhew
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

This paper describes a dataset and baseline systems for linking paragraphs from court cases to clauses or amendments in the US Constitution. We implement a rule-based system, a linear model, and a neural architecture for matching pairs of paragraphs, taking training data from online databases in a distantly-supervised fashion. In experiments on a manually-annotated evaluation set, we find that our proposed neural system outperforms a rules-driven baseline. Qualitatively, this performance gap seems largest for abstract or indirect links between documents, which suggests that our system might be useful for answering political science and legal research questions or discovering novel links. We release the dataset along with the manually-annotated evaluation set to foster future work.
Litigation Analytics: Case Outcomes Extracted from US Federal Court Dockets
Authors: Thomas Vacek, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, Frank Schilder
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Dockets contain a wealth of information for planning a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. Having such information available for every past court case, however, would be very useful for developing a strategy because it potentially reveals tendencies and trends of judges and courts and the opposing counsel. We used Natural Language Processing (NLP) techniques and deep learning methods allowing us to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations.
Developing and Orchestrating a Portfolio of Natural Legal Language Processing and Document Curation Services
Authors: Georg Rehm, Juli{\'a}n Moreno-Schneider, Jorge Gracia, Artem Revenko, Victor Mireles, Maria Khvalchik, Ilan Kernerman, Andis Lagzdins, Marcis Pinnis, Artus Vasilevskis, Elena Leitner, Jan Milde, Pia Wei{\ss}enhorn
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We present a portfolio of natural legal language processing and document curation services currently under development in a collaborative European project. First, we give an overview of the project and the different use cases, while, in the main part of the article, we focus upon the 13 different processing services that are being deployed in different prototype applications using a flexible and scalable microservices architecture. Their orchestration is operationalised using a content and document curation workflow manager.
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
Authors: Simeng Sun, Ori Shapira, Ido Dagan, Ani Nenkova
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.
Neural Text Simplification in Low-Resource Conditions Using Weak Supervision
Authors: Alessio Palmero Aprosio, Sara Tonelli, Marco Turchi, Matteo Negri, Mattia A. Di Gangi
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Machine Translation, Sentence, Text Simplification, Translation, Word Embeddings

Neural text simplification has gained increasing attention in the NLP community thanks to recent advancements in deep sequence-to-sequence learning. Most recent efforts with such a data-demanding paradigm have dealt with the English language, for which sizeable training datasets are currently available to deploy competitive models. Similar improvements on less resource-rich languages are conditioned either to intensive manual work to create training data, or to the design of effective automatic generation techniques to bypass the data acquisition bottleneck. Inspired by the machine translation field, in which synthetic parallel pairs generated from monolingual data yield significant improvements to neural models, in this paper we exploit large amounts of heterogeneous data to automatically select simple sentences, which are then used to create synthetic simplification pairs. We also evaluate other solutions, such as oversampling and the use of external word embeddings to be fed to the neural simplification system. Our approach is evaluated on Italian and Spanish, for which few thousand gold sentence pairs are available. The results show that these techniques yield performance improvements over a baseline sequence-to-sequence configuration.
Paraphrase Generation for Semi-Supervised Learning in NLU
Authors: Eunah Cho, He Xie, William M. Campbell
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Natural Language Understanding, Paraphrase Generation

Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19{\%} of relative slot error reduction without an access to user utterances, and up to 35{\%} when leveraging live traffic utterances.
Neural Text Style Transfer via Denoising and Reranking
Authors: Joseph Lee, Ziang Xie, Cindy Wang, Max Drach, Dan Jurafsky, Andrew Ng
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Denoising, Reranking, Style Transfer, Text Style Transfer

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.
Character Identification Refined: A Proposal
Authors: Labiba Jahan, Mark Finlayson
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.
Deep Natural Language Understanding of News Text
Authors: Jaya Shree, Emily Liu, Andrew Gordon, Jerry Hobbs
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Natural Language Understanding

Early proposals for the deep understanding of natural language text advocated an approach of {``}interpretation as abduction,{''} where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.
Extraction of Message Sequence Charts from Narrative History Text
Authors: Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, Vasudeva Varma
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Dependency Parsing

In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.
Unsupervised Hierarchical Story Infilling
Authors: Daphne Ippolito, David Grangier, Chris Callison-Burch, Douglas Eck
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Diversity

Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.
Identifying Sensible Lexical Relations in Generated Stories
Authors: Melissa Roemmele
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Story Generation, Text Generation

As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to {``}make sense{''} as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the {`}narrative sense{'} of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.
Clustering-Based Article Identification in Historical Newspapers
Authors: Martin Riedl, Daniela Betz, Sebastian Pad{\'o}
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Articles, Clustering, Optical Character Recognition (OCR), Segmentation, Word Embeddings

This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page. We frame the task as a segmentation plus clustering step. Our results on a sample of 1912 New York Tribune magazine shows that performing the clustering based on similarities computed with word embeddings outperforms a similarity measure based on character n-grams and words. Furthermore, the automatic segmentation based on the text results in low scores, due to the low quality of some OCRed documents.
The Scientization of Literary Study
Authors: Stefania Degaetano-Ortlieb, Andrew Piper
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Articles

Scholarly practices within the humanities have historically been perceived as distinct from the natural sciences. We look at literary studies, a discipline strongly anchored in the humanities, and hypothesize that over the past half-century literary studies has instead undergone a process of {``}scientization{''}, adopting linguistic behavior similar to the sciences. We test this using methods based on information theory, comparing a corpus of literary studies articles (around 63,400) with a corpus of standard English and scientific English respectively. We show evidence for {``}scientization{''} effects in literary studies, though at a more muted level than scientific English, suggesting that literary studies occupies a middle ground with respect to standard English in the larger space of academic disciplines. More generally, our methodology can be applied to investigate the social positioning and development of language use across different domains (e.g. scientific disciplines, language varieties, registers).
Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama
Authors: Krishnapriya Vishnubhotla, Adam Hammond, Graeme Hirst
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: General Classification, text-classification, Text Classification, Text Generation

According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors. We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries. We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters{'} utterances. Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.
Automatic Alignment and Annotation Projection for Literary Texts
Authors: Uli Steinbach, Ines Rehbein
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language. The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German. We evaluate the different components of the pipeline and discuss challenges specific to literary texts. Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.
Inferring missing metadata from environmental policy texts
Authors: Steven Bethard, Egoitz Laparra, Sophia Wang, Yiyun Zhao, Ragheb Al-Ghezi, Aaron Lien, Laura L{\'o}pez-Hoffman
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.
Stylometric Classification of Ancient Greek Literary Texts by Genre
Authors: Efthimios Gianitsos, Thomas Bolt, Pramit Chaudhuri, Joseph Dexter
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Classification, General Classification

Classification of texts by genre is an important application of natural language processing to literary corpora but remains understudied for premodern and non-English traditions. We develop a stylometric feature set for ancient Greek that enables identification of texts as prose or verse. The set contains over 20 primarily syntactic features, which are calculated according to custom, language-specific heuristics. Using these features, we classify almost all surviving classical Greek literature as prose or verse with {\textgreater}97{\%} accuracy and F1 score, and further classify a selection of the verse texts into the traditional genres of epic and drama.