Touché @ CLEF: Argument Retrieval

Touché 2021 has ended. Touché will continue with its 3rd edition at CLEF 2022. We will update our website in October 2021.

Synopsis/Call

Decision making processes, be it at the societal or at the personal level, often come to a point where one side challenges the other with a why-question, which is a prompt to justify some stance based on arguments. Since technologies for argument mining are maturing at a rapid pace, also ad-hoc argument retrieval becomes a feasible task in reach. We invite you to participate in the second Touché lab on argument retrieval at CLEF 2021 featuring two tasks. (Details on the Touché 2020 edition can be found in the overview paper or on the last year's lab web page).

Shared Tasks

Important Dates

  • November 16, 2020: Registration opens.
  • April 30, 2021: Registration closes.
  • May 7, 2021: Approaches submission deadline.
  • May 28, 2021: Participant paper submission [template] [instructions] [submit].
  • June 11, 2021: Peer review notification.
  • Mid June, 2021: Evaluation results out [results Task 1] [results Task 2].
  • July 2, 2021: Camera-ready participant papers submission [instructions].
  • September 21-24, 2021: CLEF Conference.
  • September 24, 2021: Touché Workshop on Argument Retrieval [program] .
All deadlines are 23:59 CEST.

Keynote

Yufang Hou
Theory-based Argument Quality for Advanced Argument Retrieval: Opportunities and Challenges

Bocconi University, Milan

In Argument Retrieval, not only the relevance of an argumentative text with respect to the input query needs to be assessed. To truly fulfill a user's information need within the highly convoluted arena of argumentation, other qualitative aspects, such as the appropriateness and clarity of the argument play a crucial role. These concepts relate to the so-called "theory-based" notion of argument quality, which decomposes overall quality into a series of fine-grained qualitative aspects, each relating to either the logical, rhetorical, or dialectical dimension of arguments. However, despite the fact that this notion offers the potential for more advanced and targeted argument retrieval, the landscape of research efforts on theory-based argument quality in computational argumentation is still scarce. In this talk, we discuss the current state of research on theory-based argument quality in NLP, its opportunities for argument retrieval, as well as its current and future challenges.