ArgMining 2021
The 8th Workshop on Argument Mining, 2021
co-located with EMNLP 2021, in Punta Cana, Dominican Republic

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.

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.

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.

Call for Papers

ArgMining 2021 invites the submission of long and short papers on substantial, original, and unpublished research in all aspects of argument mining.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:
Submission URL:

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:

  1. Quantitative Summarization – Key Point Analysis
  2. Frame Identificaton

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

Important Dates

  • Submission due: August 5, 2021
  • Notification of acceptance: September 5, 2021
  • Camera-ready papers due: September 15, 2021
  • Workshop: November 10-11, 2021




Organizing Committee

Khalid Al-Khatib Leipzig University
Yufang Hou
Yufang Hou IBM Research, Dublin, Ireland
Manfred Stede
Manfred Stede Potsdam University, Germany

Program Committee

  • Stergos D. Afantenos, Université Paul Sabatier
  • Rahit Aharonov, IBM Research AI
  • Carlos Alzate, AI Fund
  • Kevin Ashley, University of Pittsburgh
  • Roy Bar-Haim, IBM Research AI
  • Chris Biemann, Universität Hamburg
  • Katarzyna Budzynska, Polish National Academy of Science
  • Elena Cabrio, CNRS, Inria, I3S
  • Claire Cardie, Cornell University
  • Johannes Daxenberger, Technische Universitat Darmstadt
  • Esin Durmus, Cornell University
  • Anette Frank, University of Heidelberg
  • Iryna Gurevych, Technische Universitat Darmstadt
  • Ivan Habernal, Technische Universitat Darmstadt
  • Xinyu Hua, Northeastern University
  • Christopher Klamm, Technische Universitat Darmstadt
  • Jonas Kuhn, University of Stuttgart
  • John Lawrence, University of Dundee
  • Diane Litman, University of Pittsburgh
  • Smaranda Muresan, Columbia University
  • Elena Musi, Columbia University
  • Sebastian Pado, Universität Stuttgart
  • Joonsuk Park, University of Richmond
  • Georgios Petasis, NCSR Demokritos
  • Olesya Razuvayevskaya, ‎European Bioinformatics Institute
  • Patrick Saint-Dizier, IRIT - CNRS
  • Jodi Schneider, University of Illinois at Urbana Champaign
  • Benjamin Schiller, Technische Universitat Darmstadt
  • Benno Stein, Bauhaus-Universitat Weimar
  • Noam Slonim, IBM Research AI
  • Nicolas Turenne, University of Paris-Est Marnela-Vallee
  • Serena Villata, CNRS
  • Henning Wachsmuth, Paderborn University
  • Vern R. Walker, Maurice A. Hofstra University
  • Zhongyu Wei, Fudan University
  • Magdalena Wolska, Bauhaus-Universitat Weimar

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