Exploring the Role of Argument Structure in Online Debate Persuasion

Li, Jialu; Durmus, Esin; Cardie, Claire

doi:10.18653/v1/2020.emnlp-main.716

Cited by 26 publications

(13 citation statements)

References 32 publications

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“…Bilu et al (2019 work on the argument invention task in the debating field to automatically identify which of these arguments are relevant to the topic. Li et al (2020) explore the role of argument structure in online debate persuasion. introduce a dataset with labeled claims and work on the task of contextdependent claim detection (CDCD).…”

Section: Related Workmentioning

confidence: 99%

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

Cheng¹,

He²,

Qian³

et al. 2022

Preprint

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Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining. 1

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Section: Related Workmentioning

confidence: 99%

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

Cheng¹,

He²,

Qian³

et al. 2022

Preprint

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“…(Habernal and Gurevych, 2016;Toledo et al, 2019;Gretz et al, 2020) Thus the natural way of quantifying the success of an argument is in terms of its persuasiveness. Indeed, plenty of previous work has explored the many factors which contribute to the persuasiveness of a message: the linguistic features employed by the authors (Persing and Ng, 2017), the semantic type of claims and premises (Hidey et al, 2017), the different sources of evidence produced to support an argument (Addawood and Bashir, 2016), the effects of the personality traits and prior beliefs on persuasiveness (Lukin et al, 2017;Durmus and Cardie, 2018;, the interaction with other participants (Ji et al, 2018;Egawa et al, 2020), the use of argument invention when debating about unknown topics (Bilu et al, 2019), the structure of the arguments (Li et al, 2020), and the effect of the style of the text in achieving persuasion (El Baff et al, 2020).…”

Section: Argument Quality: An Integrated Definitionmentioning

confidence: 99%

Towards Argument Mining for Social Good: A Survey

Vecchi¹,

Falk²,

Jundi³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).

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“…They conduct experiments using argumentative essays written for the TOEFL test [8], and show that adding argumentation structure features to the model improves the performance of essay quality evaluation. • Li et al [42] enhance BERT by encoding argument structure features with the Bi-LSTM model for online debate persuasion prediction. In this case, persuasion can be viewed as a proxy for the quality of the debate text.…”

Section: Relation Linking and Quality Evaluationmentioning

confidence: 99%