pwc-archive/papers-with-abstracts huggingface dataset:
Annotating formulaic sequences in spoken Slovenian: structure, function and relevance
Authors: Kaja Dobrovoljc
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper presents the identification of formulaic sequences in the reference corpus of spoken Slovenian and their annotation in terms of syntactic structure, pragmatic function and lexicographic relevance. The annotation campaign, specific in terms of setting, subjectivity and the multifunctionality of items under investigation, resulted in a preliminary lexicon of formulaic sequences in spoken Slovenian with immediate potential for future explorations in formulaic language research. This is especially relevant for the notable number of identified multi-word expressions with discourse-structuring and stance-marking functions, which have often been overlooked by traditional phraseology research.
Annotating Information Structure in Italian: Characteristics and Cross-Linguistic Applicability of a QUD-Based Approach
Authors: Kordula De Kuthy, Lisa Brunetti, Marta Berardi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We present a discourse annotation study, in which an annotation method based on Questions under Discussion (QuD) is applied to Italian data. The results of our inter-annotator agreement analysis show that the QUD-based approach, originally spelled out for English and German, can successfully be transferred cross-linguistically, supporting good agreement for the annotation of central information structure notions such as focus and non-at-issueness. Our annotation and interannotator agreement study on Italian authentic data confirms the cross-linguistic applicability of the QuD-based approach.
DEFT: A corpus for definition extraction in free- and semi-structured text
Authors: Sasha Spala, Nicholas A. Miller, Yiming Yang, Franck Dernoncourt, Carl Dockhorn
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Definition Extraction

Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions. In reality, natural language is messy, and messy data requires both complex solutions and data that reflects that reality. In this paper, we present a robust English corpus and annotation schema that allows us to explore the less straightforward examples of term-definition structures in free and semi-structured text.
Explaining Simple Natural Language Inference
Authors: Aikaterini-Lida Kalouli, Annebeth Buis, Livy Real, Martha Palmer, Valeria de Paiva
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference

The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today{'}s research, especially in the machine learning community. This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently discussed with respect to the Natural Language Inference (NLI) task. In this work we conduct an annotation experiment based on a small subset of the SICK corpus. The experiment reveals several problems in the annotation guidelines, and various challenges of the NLI task itself. Our quantitative evaluation of the experiment allows us to assign our empirical observations to specific linguistic phenomena and leads us to recommendations for future annotation tasks, for NLI and possibly for other tasks.
On the role of discourse relations in persuasive texts
Authors: Ines Rehbein
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper investigates the use of explicitly signalled discourse relations in persuasive texts. We present a corpus study where we control for speaker and topic and show that the distribution of different discourse connectives varies considerably across different discourse settings. While this variation can be explained by genre differences, we also observe variation regarding the distribution of discourse relations across different settings. This variation, however, cannot be easily explained by genre differences. We argue that the differences regarding the use of discourse relations reflects different strategies of persuasion and that these might be due to audience design.
One format to rule them all -- The emtsv pipeline for Hungarian
Authors: Bal{\'a}zs Indig, B{\'a}lint Sass, Eszter Simon, Iv{\'a}n Mittelholcz, No{\'e}mi Vad{\'a}sz, M{\'a}rton Makrai
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: All

We present a more efficient version of the e-magyar NLP pipeline for Hungarian called emtsv. It integrates Hungarian NLP tools in a framework whose individual modules can be developed or replaced independently and allows new ones to be added. The design also allows convenient investigation and manual correction of the data flow from one module to another. The improvements we publish include effective communication between the modules and support of the use of individual modules both in the chain and standing alone. Our goals are accomplished using extended tsv (tab separated values) files, a simple, uniform, generic and self-documenting input/output format. Our vision is maintaining the system for a long time and making it easier for external developers to fit their own modules into the system, thus sharing existing competencies in the field of processing Hungarian, a mid-resourced language. The source code is available under LGPL 3.0 license at https://github.com/dlt-rilmta/emtsv .
Turkish Treebanking: Unifying and Constructing Efforts
Authors: Utku T{\"u}rk, Furkan Atmaca, {\c{S}}aziye Bet{\"u}l {\"O}zate{\c{s}}, Abdullatif K{\"o}ksal, Balkiz Ozturk Basaran, Tunga Gungor, Arzucan {\"O}zg{\"u}r
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (T{\"u}rk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.
A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets
Authors: Riya Pal, Dipti Sharma
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens labelled with Proposition Bank labels marking their semantic roles. We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels. With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data. We obtain an accuracy of 96.74{\%} for Argument Identification and are able to further classify 73.93{\%} of the labels correctly. While there is relevant ongoing research on Semantic Role Labelling and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in code-mixed data, to the best of our knowledge.
A Multi-Platform Annotation Ecosystem for Domain Adaptation
Authors: Richard Eckart de Castilho, Nancy Ide, Jin-Dong Kim, Jan-Christoph Klie, Keith Suderman
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Domain Adaptation, text annotation

This paper describes an ecosystem consisting of three independent text annotation platforms. To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognition. The platforms and the approach are in general domain-independent and can be readily applied to other areas of science.
A New Annotation Scheme for the Sejong Part-of-speech Tagged Corpus
Authors: Jungyeul Park, Francis Tyers
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Morphological Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging

In this paper we present a new annotation scheme for the Sejong part-of-speech tagged corpus based on Universal Dependencies style annotation. By using a new annotation scheme, we can produce Sejong-style morphological analysis and part-of-speech tagging results which have been the \textit{de facto} standard for Korean language processing. We also explore the possibility of doing named-entity recognition and semantic-role labelling for Korean using the new annotation scheme.
A Turkish Dataset for Gender Identification of Twitter Users
Authors: Erhan Sezerer, Ozan Polatbilek, Selma Tekir
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Author Profiling

Author profiling is the identification of an author{'}s gender, age, and language from his/her texts. With the increasing trend of using Twitter as a means to express thought, profiling the gender of an author from his/her tweets has become a challenge. Although several datasets in different languages have been released on this problem, there is still a need for multilingualism. In this work, we propose a dataset of tweets of Turkish Twitter users which are labeled with their gender information. The dataset has 3368 users in training set and 1924 users in test set where each user has 100 tweets. The dataset is publicly available.
Comparative judgments are more consistent than binary classification for labelling word complexity
Authors: Sian Gooding, Ekaterina Kochmar, Advait Sarkar, Alan Blackwell
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Binary Classification, Complex Word Identification, General Classification, Lexical Simplification

Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context. We explore how users can help train complex word identification models through labelling more efficiently and reliably. We show that using an interface where annotators make comparative rather than binary judgments leads to more reliable and consistent labels, and explore whether comparative judgments may provide a faster way for collecting labels.
Continuous Quality Control and Advanced Text Segment Annotation with WAT-SL 2.0
Authors: Christina Lohr, Johannes Kiesel, Stephanie Luther, Johannes Hellrich, Tobias Kolditz, Benno Stein, Udo Hahn
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Today{'}s widely used annotation tools were designed for annotating typically short textual mentions of entities or relations, making their interface cumbersome to use for long(er) stretches of text, e.g, sentences running over several lines in a document. They also lack systematic support for hierarchically structured labels, i.e., one label being conceptually more general than another (e.g., anamnesis in relation to family anamnesis). Moreover, as a more fundamental shortcoming of today{'}s tools, they provide no continuous quality con trol mechanisms for the annotation process, an essential feature to intrinsically support iterative cycles in the development of annotation guidelines. We alleviated these problems by developing WAT-SL 2.0, an open-source web-based annotation tool for long-segment labeling, hierarchically structured label sets and built-ins for quality control.
Creation of a corpus with semantic role labels for Hungarian
Authors: Attila Nov{\'a}k, L{\'a}szl{\'o} Laki, Borb{\'a}la Nov{\'a}k, Andrea D{\"o}m{\"o}t{\"o}r, No{\'e}mi Ligeti-Nagy, {\'A}gnes Kalivoda
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In this article, an ongoing research is presented, the immediate goal of which is to create a corpus annotated with semantic role labels for Hungarian that can be used to train a parser-based system capable of formulating relevant questions about the text it processes. We briefly describe the objectives of our research, our efforts at eliminating errors in the Hungarian Universal Dependencies corpus, which we use as the base of our annotation effort, at creating a Hungarian verbal argument database annotated with thematic roles, at classifying adjuncts, and at matching verbal argument frames to specific occurrences of verbs and participles in the corpus.
Towards a General Abstract Meaning Representation Corpus for Brazilian Portuguese
Authors: Marco Antonio Sobrevilla Cabezudo, Thiago Pardo
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation

Abstract Meaning Representation (AMR) is a recent and prominent semantic representation with good acceptance and several applications in the Natural Language Processing area. For English, there is a large annotated corpus (with approximately 39K sentences) that supports the research with the representation. However, to the best of our knowledge, there is only one restricted corpus for Portuguese, which contains 1,527 sentences. In this context, this paper presents an effort to build a general purpose AMR-annotated corpus for Brazilian Portuguese by translating and adapting AMR English guidelines. Our results show that such approach is feasible, but there are some challenging phenomena to solve. More than this, efforts are necessary to increase the coverage of the corresponding lexical resource that supports the annotation.
Co-Operation as an Asymmetric Form of Human-Computer Creativity. Case: Peace Machine
Authors: Mika H{\"a}m{\"a}l{\"a}inen, Timo Honkela
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Form

This theoretical paper identifies a need for a definition of asymmetric co-creativity where creativity is expected from the computational agent but not from the human user. Our co-operative creativity framework takes into account that the computational agent has a message to convey in a co-operative fashion, which introduces a trade-off on how creative the computer can be. The requirements of co-operation are identified from an interdisciplinary point of view. We divide co-operative creativity in message creativity, contextual creativity and communicative creativity. Finally these notions are applied in the context of the Peace Machine system concept.
DSTC7 Task 1: Noetic End-to-End Response Selection
Authors: Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter Lasecki
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Conversational Response Selection, Goal-Oriented Dialogue Systems

Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.
End-to-End Neural Context Reconstruction in Chinese Dialogue
Authors: Wei Yang, Rui Qiao, Haocheng Qin, Amy Sun, Luchen Tan, Kun Xiong, Ming Li
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: coreference-resolution, Coreference Resolution, POS, Position, Referring Expression

We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context. Following a standard decomposition of the context reconstruction task into referring expression detection and coreference resolution, we propose a novel end-to-end architecture for separately and jointly accomplishing this task. Key features of this model include POS and position encoding using CNNs and a novel pronoun masking mechanism. One perennial problem in building such models is the paucity of training data, which we address by augmenting previously-proposed methods to generate a large amount of realistic training data. The combination of more data and better models yields accuracy higher than the state-of-the-art method in coreference resolution and end-to-end context reconstruction.
Energy-Based Modelling for Dialogue State Tracking
Authors: Anh Duong Trinh, Robert Ross, John Kelleher
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Deep Learning, Dialogue State Tracking

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.
Insights from Building an Open-Ended Conversational Agent
Authors: Khyatti Gupta, Meghana Joshi, Ankush Chatterjee, Sonam Damani, Kedhar Nath Narahari, Puneet Agrawal
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abusive Language

Dialogue systems and conversational agents are becoming increasingly popular in modern society. We conceptualized one such conversational agent, Microsoft{'}s {``}Ruuh{''} with the promise to be able to talk to its users on any subject they choose. Building an open-ended conversational agent like Ruuh at onset seems like a daunting task, since the agent needs to think beyond the utilitarian notion of merely generating {``}relevant{''} responses and meet a wider range of user social needs, like expressing happiness when user{'}s favourite sports team wins, sharing a cute comment on showing the pictures of the user{'}s pet and so on. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behaviour of the users. Many of these problems pose significant research challenges as well as product design limitations as one needs to circumnavigate the technical limitations to create an acceptable user experience. However, as the product reaches the real users the true test begins, and one realizes the challenges and opportunities that lie in the vast domain of conversations. With over 2.5 million real-world users till date who have generated over 300 million user conversations with Ruuh, there is a plethora of learning, insights and opportunities that we will talk about in this paper.
Relevant and Informative Response Generation using Pointwise Mutual Information
Authors: Junya Takayama, Yuki Arase
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Response Generation

A sequence-to-sequence model tends to generate generic responses with little information for input utterances. To solve this problem, we propose a neural model that generates relevant and informative responses. Our model has simple architecture to enable easy application to existing neural dialogue models. Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance. Then, the model encourages the decoder to use the keywords for response generation. Experiment results demonstrate that our model successfully diversifies responses relative to previous models.
Responsive and Self-Expressive Dialogue Generation
Authors: Kozo Chikai, Junya Takayama, Yuki Arase
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Dialogue Generation, Feature Engineering

A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like {``}OK{''} and {``}I don{'}t know.{''} Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1{\%} of our responses were domain-aware with sentiment polarities, which was only 2.7{\%} for responses generated by the sequence-to-sequence model.
AX Semantics' Submission to the SIGMORPHON 2019 Shared Task
Authors: Andreas Madsack, Robert Wei{\ss}graeber
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder

This paper describes the AX Semantics{'} submission to the SIGMORPHON 2019 shared task on morphological reinflection. We implemented two systems, both tackling the task for all languages in one codebase, without any underlying language specific features. The first one is an encoder-decoder model using AllenNLP; the second system uses the same model modified by a custom trainer that trains only with the target language resources after a specific threshold. We especially focused on building an implementation using AllenNLP with out-of-the-box methods to facilitate easy operation and reuse.
Cognate Projection for Low-Resource Inflection Generation
Authors: Bradley Hauer, Amir Ahmad Habibi, Yixing Luan, Rashed Rubby Riyadh, Grzegorz Kondrak
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We propose cognate projection as a method of crosslingual transfer for inflection generation in the context of the SIGMORPHON 2019 Shared Task. The results on four language pairs show the method is effective when no low-resource training data is available.