Estimating Subjective Argument Quality Aspects From Social Signals in Argumentative Dialogue Systems

Rach, Niklas; Matsuda, Yuki; Ultes, Stefan; Minker, Wolfgang; Yasumoto, Keiichi

doi:10.1109/access.2021.3051526

Cited by 6 publications

(6 citation statements)

References 36 publications

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“…They all rank arguments by relevance to the user-specified topic, while some of them present extra information for each argument such as supporting evidence, its stance score (denoting the extent to which it supports or refutes the claim) or its relevance score. While these are closer to the kinds of applications we envision for the Web of Debates, their performance is still limited as, for example, evidenced by the results of a recent user-based evaluation, which showed that they do not significantly outperform conventional search engines especially with respect to the convincingness of the arguments they retrieve (Rach et al, 2020). This can be attributed on the one hand to the limitations of the argument mining methods they use, and on the other to the lack of a method to assess the quality or persuasiveness of arguments.…”

Section: Motivating Examplementioning

confidence: 99%

Sketching the vision of the Web of Debates

Bikakis

Flouris

Patkos

et al. 2023

Front. Artif. Intell.

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The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs.

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Section: Motivating Examplementioning

confidence: 99%

Sketching the vision of the Web of Debates

Bikakis

Flouris

Patkos

et al. 2023

Front. Artif. Intell.

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“…Dialogue Systems In the realm of visualization, a novel approach gaining attraction is the integration of dialogue systems to enhance the interaction between users and visual representations. Dialogue systems, commonly known as chatbots like ChatGPT, have been increasingly explored for their potential to facilitate information comprehension (Rach et al, 2020;Wambsganß et al, 2021).…”

Section: Visualization -How To Show the Error?mentioning

confidence: 99%

Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation

Guerraoui,

Reisert,

Inoue

et al. 2023

Proceedings of the 10th Workshop on Argument Mining

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The use of argumentation in education has shown improvement in students' critical thinking skills, and computational models for argumentation have been developed to further assist this process. Although these models are useful for evaluating the quality of an argument, they often cannot explain why a particular argument score was predicted, i.e., why the argument is good or bad, which makes it difficult to provide constructive feedback to users, e.g., students, so that they can strengthen their critical thinking skills. In this survey, we explore current NLP feedback systems by categorizing each into four important dimensions of feedback (Richness, Visualization, Interactivity and Personalization). We discuss limitations for each dimension and provide suggestions to enhance the power of feedback and explanations to ultimately improve user critical thinking skills.

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“…Systems introduced so far include the one developed in the scope of IBM project debater , Argu-menText (Stab et al, 2018), args.me (Wachsmuth et al, 2017b), TARGER (Chernodub et al, 2019) and PerspectroScope . The general applicability of argument search engines in the context of dialogue systems was assessed in (Rach et al, 2020a) where ArgumenText and args.me were compared to a baseline system. Although a mapping into argument structures was not addressed, we use the discussed results to select a suitable search engine for the present work.…”

Section: General Approachmentioning

confidence: 99%

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

2021

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We are glad to pen the first few words for the proceedings of SIGDIAL 2021, the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue. The SIGDIAL conference is a premier publication venue for research in discourse and dialogue. This year, the conference is organized as a hybrid event with both in-person and virtual participation on July 29-31, 2021, right before ACL-IJCNLP 2021. The 2021 Young Researchers' Roundtable on Spoken Dialog Systems (YRRSDS 2021) is also held as a satellite event. The SIGDIAL 2021 program features three keynote talks, 6 paper presentation sessions, 1 demo session, and 2 special sessions, entitled "Summarization of Dialogues and Multi-Party Meetings", and "Safety for E2E Conversational AI".COVID has changed the way we work, but it doesn't hamper our research progress. We received 142 submissions this year, comprising 88 long papers, 49 short papers, and 5 demo descriptions. We had 12 Senior Program Committee (SPC) members who were each responsible for 11-12 papers, leading the discussion process and also contributing meta-reviews. Each submission was assigned to an SPC member and received at least three reviews. Decisions carefully considered the original reviews, meta-reviews, and discussions among reviewers facilitated by the SPCs. We are immensely grateful to the members of the Program Committee and Senior Program Committee for efforts in providing excellent, thoughtful reviews of the large number of submissions. Their contributions have been essential to selecting the accepted papers and providing a high-quality technical program for the conference. We have aimed to develop a broad, varied program spanning the many positively rated papers identified by the review process. We accepted 59 papers in total: 40 long papers (45%), 15 short papers (31%), and 4 demo descriptions, for an overall acceptance rate of 41.5%, in line with prior years. One keynote will highlight each of the three days of the conference. In organizing this hybrid inperson/virtual conference, we have tried to maintain as much of the spirit of a fully online conference as possible. Recordings for all papers and demos have been made available several days before the start of the conference, for participants to watch asynchronously. Long and short papers are organized into sessions taking into consideration the presenters' time zones. Regular papers sessions span 8-11 papers, each presented as a two-minute pre-recorded talk followed by five minutes of live Q&A. For demos, we organized four parallel zoom rooms to allow participants to interact with and observe live interactions with the systems. The topics represent the breadth of research in discourse and dialogue. A conference of this size requires the energy, guidance, and contributions of many parties, and we would like to take this opportunity to thank and acknowledge them all. We thank our three keynote speakers, Julia Hirschberg (Columbia University), Raymond J. Mooney (University of Texas at Austin), and Jason Weston (Facebook AI & NYU), for...

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Estimating Subjective Argument Quality Aspects From Social Signals in Argumentative Dialogue Systems

Cited by 6 publications

References 36 publications

Sketching the vision of the Web of Debates

Sketching the vision of the Web of Debates

Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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