Argumentation Mining in Parliamentary Discourse

Naderi, Nona; Hirst, Graeme

doi:10.1007/978-3-319-46218-9_2

Cited by 19 publications

(18 citation statements)

References 13 publications

(16 reference statements)

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“…Other works classify argumentation schemes (Feng et al, 2014) and frames (Naderi and Hirst, 2015), analyze overall argumentation structures (Wachsmuth et al, 2015), or generate claims (Bilu and Slonim, 2016). Also, theories of argumentation quality exist, and some quality dimensions have been assessed computationally (see Section 2 for details).…”

Section: Introductionmentioning

confidence: 99%

Computational Argumentation Quality Assessment in Natural Language

Wachsmuth¹,

Naderi²,

Hou³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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187

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Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

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Section: Introductionmentioning

confidence: 99%

Computational Argumentation Quality Assessment in Natural Language

Wachsmuth¹,

Naderi²,

Hou³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

Self Cite

121

187

View full text Add to dashboard Cite

show abstract

“…Annotating a large corpus by hand is a tedious task and little existing work in argument mining has explored alternative ways to do it. Naderi and Hirst (2014) propose a framebased approach for dealing with arguments present in parliamentary discourse and suggest that using a semi-supervised approach can help in developing their dataset into a large corpus. Habernal and Gurevych (2015) have proposed a semisupervised based approach for identifying arguments using a clustering based approach on unlabelled data.…”

Section: Related Workmentioning

confidence: 99%

Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset

Rajendran

Bollegala

Parsons

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Online reviews have become a popular portal among customers making decisions about purchasing products. A number of corpora of reviews have been widely investigated in NLP in general, and, in particular, in argument mining. This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content. A major problem faced by argument mining research is the lack of human-annotated data. In this paper, we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data, and thus providing large annotated datasets. We do this by building on previous work that explores the classification of opinions present in reviews based on whether the stance is expressed explicitly or implicitly. In the work described here, we automatically annotate stance as implicit or explicit and our results show that the datasets we generate, although noisy, can be used to learn better models for implicit/explicit opinion classification.

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“…One approach categorizes sentences or posts using topic-specific argument labels, which are functionally similar to our facets as discussed above (Conrad et al, 2012; Hasan and Ng, 2014; Boltuzic andŠnajder, 2014;Naderi and Hirst, 2015). For example, Fig.…”

Section: Related Workmentioning

confidence: 99%

“…2. 3 Previous work then labels posts or sentences using these facets, and trains a classifier to return a facet label (Conrad et al, 2012;Hasan and Ng, 2014;Boltuzic andŠnajder, 2014;Naderi and Hirst, 2015), inter alia. However, this simplification may not work in the long term, both because the sentential realizations of argument facets are propositional, and hence graded, and because facets evolve over time, and hence cannot be represented by a finite list.…”

Section: S1mentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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Argumentation Mining in Parliamentary Discourse

Cited by 19 publications

References 13 publications

Computational Argumentation Quality Assessment in Natural Language

Computational Argumentation Quality Assessment in Natural Language

Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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