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| "paper_id": "2021", | |
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| "text": "Argument mining (also known as \"argumentation mining\") is a young and gradually maturing research area within computational linguistics. At its heart, argument mining involves the automatic identification of argumentative structures in free text, such as the conclusions, premises, and inference schemes of arguments as well as their interrelations and counter-considerations. To date, researchers have investigated argument mining on genres such as legal documents, product reviews, news articles, online debates, user-generated web discourse, Wikipedia articles, academic literature, persuasive essays, tweets, and dialogues. Recently, also argument quality assessment and generation came into focus. In addition, argument mining is inherently tied to stance and sentiment analysis, since every argument carries a stance towards its topic, often expressed with sentiment.", | |
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| "section": "Introduction", | |
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| "text": "Argument mining gives rise to various practical applications of great importance. In particular, it provides methods that can find and visualize the main pro and con arguments in a text corpus -or even on in an argument search on the web -towards a topic or query of interest. In instructional contexts, written and diagrammed arguments represent educational data that can be mined for conveying and assessing students' command of course material. In information retrieval, argument mining is expected to play a salient role in the emerging field of conversational search. And with the IBM Debater Project, technology based on argument mining recently received a lot of media attention.", | |
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| "section": "Introduction", | |
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| "text": "While solutions to basic tasks such as component segmentation and classification slowly become mature, many tasks remain largely unsolved, particularly in more open genres and topical domains. Success in argument mining requires interdisciplinary approaches informed by NLP technology, theories of semantics, pragmatics and discourse, knowledge of discourse in application domains, artificial intelligence, information retrieval, argumentation theory, and computational models of argumentation.", | |
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| "section": "Introduction", | |
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| "text": "The community around ArgMining is constantly growing. This year's edition of the workshop had 39 valid submissions (27 in 2017, 32 in 2018, 41 in 2019, and 30 in 2020). Among the submitted papers, there were 23 full papers, 9 short papers, and 7 shared-task papers. Out of the 39 papers, 11 full papers, 4 short papers, and 6 shared-task papers have been accepted, resulting in an overall acceptance rate of 54%. All the papers are included in the proceedings at hand.", | |
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| "section": "Introduction", | |
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| "text": "Given the duration of the workshop (1.5 days) and its format (hybrid), we decided to give all the authors the possibility to present their work orally. Long papers were given 15 min for the talk and 5 min for the discussion, while the short papers were given 10 min for the talk and 2 min for discussion. We were delighted to have Professors Anthony Hunter from University College London and Lu Wang from the University of Michigan as keynote speakers. The speakers addressed interesting topics related to persuasion and argument generation. ", | |
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| "section": "Introduction", | |
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| "text": "November 10, 2021 (continued) ", | |
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| "section": "annex", | |
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| "content": "<table><tr><td/><td>Workshop Program</td></tr><tr><td>November 10, 2021</td><td/></tr><tr><td colspan=\"2\">Organizers: Khalid Al-Khatib, Bauhaus-Universit\u00e4t, Weimar (chair) 09:00 -09:30 Opening Remarks</td></tr><tr><td colspan=\"2\">Yufang Hou, IBM Research Dublin (chair) Manfred Stede, Universit\u00e4t Potsdam (chair) 09:30 -10:30 Invited Talk 1</td></tr><tr><td>10:30 -11:00</td><td>Coffee Break</td></tr><tr><td/><td>Session 1</td></tr><tr><td>11:00 -11:20</td><td>Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate</td></tr><tr><td/><td>Muhammad Mahad Afzal Bhatti, Ahsan Suheer Ahmad and Joonsuk Park</td></tr><tr><td>11:20 -11:40</td><td>Multi-task and Multi-corpora Training Strategies to Enhance Argumentative Sen-</td></tr><tr><td/><td>tence Linking Performance</td></tr><tr><td/><td>Jan Wira Gotama Putra, Simone Teufel and Takenobu Tokunaga</td></tr><tr><td>11:40 -12:00</td><td>Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Rep-</td></tr><tr><td/><td>resentation and Conclusion Generation</td></tr><tr><td/><td>Juri Opitz, Philipp Heinisch, Philipp Wiesenbach, Philipp Cimiano and Anette</td></tr><tr><td/><td>Frank</td></tr><tr><td>12:00 -13:00</td><td>Lunch Break</td></tr><tr><td/><td>Session 2</td></tr><tr><td>13:00 -13:20</td><td>Knowledge-Enhanced Evidence Retrieval for Counterargument Generation</td></tr><tr><td/><td>Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed and Eduard Hovy</td></tr><tr><td>13:20 -13:40</td><td>On Classifying whether Two Texts are on the Same Side of an Argument</td></tr><tr><td/><td>Erik K\u00f6rner, Gregor Wiedemann, Ahmad Hakimi, Gerhard Heyer and Martin Pot-</td></tr><tr><td/><td>thast</td></tr><tr><td>13:40 -13:52</td><td>Multilingual Counter Narrative Type Classification</td></tr><tr><td/><td>Yi-Ling Chung, Marco Guerini and Rodrigo Agerri</td></tr><tr><td>13:52 -13:64</td><td>Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?</td></tr><tr><td/><td>Neele Falk, Iman Jundi, Eva Maria Vecchi and Gabriella Lapesa</td></tr><tr><td>14:04 -14:16</td><td>Self-trained Pretrained Language Models for Evidence Detection</td></tr><tr><td/><td>Mohamed Elaraby and Diane Litman</td></tr><tr><td>14:16 -14:28</td><td>Multi-task Learning in Argument Mining for Persuasive Online Discussions</td></tr><tr><td/><td>Nhat Tran and Diane Litman</td></tr><tr><td>14:30 -14:45</td><td>Coffee Break</td></tr><tr><td>14:45 -16:15</td><td>Panel Talks and Discussion</td></tr><tr><td colspan=\"2\">Khalid Al-Khatib, Yufang Hou, and Manfred Stede (ArgMining 2021 co-chairs) 16:15 -16:45 Coffee Break</td></tr></table>", | |
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| "text": "The ArgMining 2021 workshop program covered the Quantitative Summarization-Key Point Analysis Shared Task, and also featured a best paper award, thankfully sponsored by IBM. Both the shared task and award are announced on the official workshop website chaired by Roxanne El Baff: https://2021.argmining.org/." | |
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