| Challenge: | Existing methods for eliciting information from user opinion data are limited to high-level text and are prone to hallucination, degrading system performance or introduce biases. |
| Approach: | They propose an argumentation annotation scheme that models argumentative structure across user opinion domains. |
| Outcome: | The proposed model can predict arguments and contextual details from user opinions . the model can rank products based on user opinions and improve user experience . |
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
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Annotating Arguments in a Corpus of Opinion Articles (2022.lrec-1)
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Gil Rocha, Luís Trigo, Henrique Lopes Cardoso, Rui Sousa-Silva, Paula Carvalho, Bruno Martins, Miguel Won
| Challenge: | Argument annotation is the process of exposing and justifying one's points of view, with the aim of conveying a logical reasoning through a set of semantically related propositions. |
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| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
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A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)
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| Challenge: | Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data. |
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Belief-based Generation of Argumentative Claims (2021.eacl-main)
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| Challenge: | Existing methods to generate argument with the ability to encode beliefs are limited by the noise generated by the automatic collection of bag-of-words. |
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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
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| Challenge: | Argumentation is an omnipresent rudiment of daily communication and thinking . humans struggle to develop argumentation skills due to a lack of individual and instant feedback in their learning process. |
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Unveiling the Power of Argument Arrangement in Online Persuasive Discussions (2023.findings-emnlp)
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| Challenge: | a recent study shows that the CMV is the best time period in human history for the vast majority of people. |
| Approach: | They extend a semantic argumentation unit type model by clustering type sequences into different argument arrangement patterns and representing discussions as sequences of these patterns. |
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A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
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| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
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