ArgMining 2021 invites the submission of long and short papers on substantial, original, and unpublished research in all aspects of argument mining. The workshop solicits LONG and SHORT papers for oral and poster presentations, as well as DEMOS of argument/argumentation mining systems and tools.

The topics for submissions include but are not limited to:

Automatic identification of argument components (e.g., premises and conclusions), and relations between arguments (e.g., support and attack) in as well as across documents
Automatic assessment of properties of arguments and argumentation, such as argumentation schemes, stance, quality, and persuasiveness
Creation and evaluation of argument annotation schemes, developing automatic and semi-automatic argument annotation methods and tools, and building high-quality annotated datasets, benchmarks, and Knowledge graphs
Automatic retrieval, summarization, and generation of arguments
Applications of argument mining and computational argumentation to various domains and data such as social sciences and humanities texts, legal and technical documents, scientific papers, news corpora, Wikipedia articles, consumer reviews, user-generated content, and students’ written essays.

This year, the workshop plans to have a joint session with the workshop CODI (Computational Approaches to Discourse). Submissions that address argumentation from an angle that overlaps with discourse structure phenomena will be considered for that session. (Authors may but need not make this potential overlap explicit)


Submission Information

Three types of papers can be submitted: Long papers (8 pages + references), short papers (4 pages + references), and demo papers (4 pages + references). Demo papers must include a URL to a running demo. Accepted papers will be given an additional page to account for the reviewers' comments. All papers will be treated equally in the workshop proceedings. The workshop follows ACL’s policies for submission, review, and citation. Moreover, authors are expected to adhere to the ethical code set out in the ACL Code of Ethics. Submissions that violate any of the policies will be rejected without review.

Please use the EMNLP 2021 style sheets for formatting your paper: https://2021.emnlp.org/
Submission URL: https://www.softconf.com/emnlp2021/ArgMining/

The workshop is running a double-blind review process. In preparing your manuscript, do not include any information which could reveal your identity, or that of your co-authors. The title section of your manuscript should not contain any author names, email addresses, or affiliation status. If you do include any author names on the title page, your submission will be automatically rejected. In the body of your submission, you should eliminate all direct references to your own previous work. That is, avoid phrases such as "this contribution generalizes our results for XYZ". Also, please do not disproportionately cite your own previous work. In other words, make your submission as anonymous as possible. We need your cooperation in our effort to maintain a fair, double-blind reviewing process - and to consider all submissions equally. Double Submission Papers that have been or will be submitted to other venues should indicate this at submission time. Upon acceptance at either event, the submission must be withdrawn from the other. To save reviewers' efforts, avoid submitting (or withdraw early) papers that are on track to be accepted elsewhere.
Shared Tasks

ArgMining 2021 includes the following shared tasks:

Quantitative Summarization – Key Point Analysis

For detailed information about the tasks, data, evaluation, and organisers, please see the shared tasks page.
Important Dates

Submission due: August 20, 2021 **Extended Deadline**
Notification of acceptance: September 15, 2021
Camera-ready papers due: September 23, 2021
Workshop: November 10-11, 2021


Schedule

November 10, 2021
09:15 – 09:30 Opening Remarks
09:30 – 10:30 Invited Talk: Towards Computational Persuasion for Behaviour Change Applications by Anthony Hunter
10:30 – 11:00 Coffee Break
11:00–12:00 Session 1
11:00 – 11:20 long Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate
Muhammad Mahad Afzal Bhatti, Ahsan Suheer Ahmad and Joonsuk Park
11:20 – 11:40 long Multi-task and Multi-corpora Training Strategies to Enhance Argumentative Sentence Linking Performance
Jan Wira Gotama Putra, Simone Teufel and Takenobu Tokunaga
11:40 – 12:00 long Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation
Juri Opitz, Philipp Heinisch, Philipp Wiesenbach, Philipp Cimiano and Anette Frank
12:00 – 13:00 Lunch Break
13:00 – 16:45 Session 2
13:00 – 13:20 finding Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed and Eduard Hovy
13:20 – 13:40 finding On Classifying whether Two Texts are on the Same Side of an Argument
Erik Körner, Gregor Wiedemann, Ahmad Hakimi, Gerhard Heyer and Martin Potthast
13:40 – 13:52 short Multilingual Counter Narrative Type Classification
Yi-Ling Chung, Marco Guerini and Rodrigo Agerri
13:52 – 13:04 short Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?
Neele Falk, Iman Jundi, Eva Maria Vecchi and Gabriella Lapesa
14:04 – 14:16 short Self-trained Pretrained Language Models for Evidence Detection
Mohamed Elaraby and Diane Litman
14:16 – 14:28 short Multi-task Learning in Argument Mining for Persuasive Online Discussions
Nhat Tran and Diane Litman
14:30 – 14:45 Coffee Break
14:45 – 16:15 Panel Talks and Discussion
16:15 – 16:45 Coffee Break
16:45–17:45 Session 3
16:45 – 17:05 long Image Retrieval for Arguments Using Stance-Aware Query Expansion
Johannes Kiesel, Nico Reichenbach, Benno Stein and Martin Potthast
17:05 – 17:25 long Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study
Myrthe Reuver, Suzan Verberne, Roser Morante and Antske Fokkens
17:25 – 17:45 long Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments
Keshav Singh, Farjana Sultana Mim, Naoya Inoue, Shoichi Naito and Kentaro Inui
November 11, 2021
09:00 – 10:00 Invited Talk: Convince Me If You Can: Natural Language Generation for Argumentation by Lu Wang
10:00–11:00 Session 4
10:00 – 10:20 long Assessing the Sufficiency of Arguments through Conclusion Generation
Timon Gurcke, Milad Alshomary and Henning Wachsmuth
10:20 – 10:40 long M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts
Rafael Mestre, Razvan Milicin, Stuart E. Middleton, Matt Ryan, Jiatong Zhu and Timothy J. Norman
10:40 – 11:00 Coffee Break
11:00–12:00 Session 5
11:00 – 11:20 long Citizen Involvement in Urban Planning - How Can Municipalities Be Supported in Evaluating Public Participation Processes for Mobility Transitions?
Julia Romberg and Stefan Conrad
11:20 – 11:40 long Argumentation Mining in Scientific Literature for Sustainable Development
Aris Fergadis, Dimitris Pappas, Antonia Karamolegkou and Haris Papageorgiou
11:40 – 12:00 long Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes
Takahiro Kondo, Koki Washio, Katsuhiko Hayashi and Yusuke Miyao
12:00 – 13:00 Lunch Break
13:00 – 14:30 Shared Task Presentation
14:30 – 14:45 Break
14:45 – 15:00 IBM Debater API
15:00 – 15:05 Best Paper Announcement
15:05 – 15:10 Concluding Remarks

* The schedule is based on the Atlantic Standard Time (Punta Cana, Dominican Republic (GMT-4)).
Keynote Speakers

Anthony Hunter
Anthony Hunter

Anthony Hunter is a Professor of Artificial Intelligence in the Department of Computer Science, University College London. He is also Head of the Intelligent Systems Group, and Department Graduate Tutor. His research is in the area of machine reasoning which is a branch of artificial intelligence. Reasoning is a critically important faculty of human intelligence, and machine reasoning is concerned with capturing aspects of this for use in software.

Talk: Towards Computational Persuasion for Behaviour Change Applications.

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). Recent developments in computational modelling of argument (a subfield of AI) are leading to technology for persuasion that can potentially be harnessed in behaviour change applications. Using this technology, a software system and a user can exchange arguments in a dialogue. So the system gains information about the user’s perspective, provides arguments to fill gaps in the user’s knowledge, and attempts to overturn misconceptions held by the user. Our work has focused on modelling the beliefs and concerns of the user, and harnessing these to make the best choices of move during the dialogue for persuading the user to change their behaviour. During this talk, the background to, and components of, our approach will be presented together with some promising preliminary results with participants.
Lu Wang
Lu Wang

Lu Wang is an Assistant Professor of Computer Science and Engineering at University of Michigan. Lu's research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.

Talk: Convince Me If You Can: Natural Language Generation for Argumentation.

Understanding, evaluating, and generating arguments are crucial elements of the decision-making and reasoning process. A multitude of arguments and counter-arguments are constructed on a daily basis to persuade and inform us on a wide range of issues. However, constructing persuasive arguments is a challenging task for both human and computers, as it requires credible evidence, rigorous logical reasoning, and sometimes emotional appeals.
In this talk, I will introduce our neural network-based argument generation framework. Our two-step argument generation model separately tackles the challenges of content planning and surface realization, and can be optionally augmented with a powerful retrieval system. I then discuss how to extend the model to conduct dynamic content planning with mixed language models. We believe that the proposed argument generation framework will enable many compelling applications, including providing unbiased perspectives on complex issues, debate coaching, and essay writing tutoring. Our framework is also generic and has been applied to other text generation problems, e.g., writing news and Wikipedia articles.