[Corpora-List] Second Call for Papers: Unshared Task at ACL 2016 Workshop on Argument Mining

Habernal, Ivan habernal at ukp.informatik.tu-darmstadt.de
Mon Apr 25 09:46:26 CEST 2016


(apologies for cross-posting)

2nd Call for Papers: Unshared Task at ACL 2016 Workshop on Argument Mining

Deadline for all categories of submission: 8 May, 2016 at midnight Samoa time [UTC-11]

Introduction ============

Argument mining (also known as “argumentation mining”) is a recent challenge in corpus-based discourse processing that applies a certain argumentation theory to model and automatically analyze the data at hand. Interest in this topic has rapidly increased over the past few years, as demonstrated by a number of events (Workshops on Argumentation Mining at ACL 2014, NAACL 2015, and ACL 2016; Dagstuhl Seminar on Debating Technologies in 2015; and Frontiers and Connections between Argumentation Theory and Natural Language Processing in 2014, to name a few). Given the wide the range of different perspectives and approaches within the community, it is now very important to consolidate the view and shape the further development of argument mining.

Shared tasks have have become a major driver in boosting research in many NLP fields. The availability of shared annotated data and clear evaluation criteria, and the presence of several competing systems, allow for fair and exact comparison and overall progress tracking, which in turn fosters future research. However, argument mining, as an evolving research field, suffers not only from the lack of large data sets but also from the absence of a unified perspective on the tasks to be fulfilled as well as their evaluation. Given the variety of argumentation models, various argumentative genres and registers, granularity (i.e., micro-argumentation and macro-argumentation), dimensions of argument (logos, pathos, ethos), and the overall social context of persuasion, the goal of defining a widely accepted task requires a substantial agreement, supported by empirical evidence.

The concept of a so-called “unshared task” is an alternative to shared tasks. In an unshared task, neither quantitative performance measures nor a clearly defined problem to be solved is given. Instead, participants are given only raw unannotated data and an open-ended prompt. The goals are to explore possible tasks, provide a rigorous definition, propose an annotation methodology, and define evaluation metrics, among others. This type of activity has been successful in several areas, such as PoliInformatics (a broad group of computer scientists, political scientists, economists, and communications scholars) (Smith et al., 2014) and clinical psychology and NLP (Coppersmith et al., 2015). Some venues also run shared and unshared tasks in parallel (Zampieri et al., 2015). The nature of an unshared task is mainly exploratory and can eventually lead to a deeper understanding of the matter and to laying down foundations for a future standard shared task.

Goals =====

Given a corpus of various argumentative raw texts, the aim of this unshared task is to tackle several fundamental research questions in argument mining. In short, the participants should propose a task with a corresponding annotation model (scheme), conduct an annotation experiment, and critically evaluate the overall strengths and weaknesses of the task. In particular, the participants should focus on:

- Proposing a task to be tackled, supported by a strong motivation for it and accompanied by a rigorous definition and thorough explanation. We encourage (but do not require) going beyond existing tasks in argumentation mining research (such as argument component identification, argument structure parsing, and stance detection). Some novel perspectives might involve capturing more of the pragmatic layer (such as the activity, purpose, or roles), user interactions, attribution, or patterns of debating. On the other hand, tasks involving detecting claims and premises have already been applied across registers and domains, so they might also serve as a basis for further elaboration.

- Properly describing the annotation scheme and its grounding in existing research in argumentation or communication studies (if applicable). This might involve applying an existing model of argument/argumentation (Toulmin's, Walton's, Dung's, Pragma-Dialectic) and its critical assessment by empirical evidence, or a newly created annotation scheme that fits the task at hand.

- Assessing the strengths and weaknesses of the annotation scheme with respect to its dependence on domain/register and ease or difficulty to apply while annotating the data. We suggest reporting inter-annotator agreement. Experimenting with various annotation set-ups, such as expert-, layman-, or crowdsourcing-based ones, is also encouraged, where applicable. The level of required expertise is an important factor for any annotation project as well as the requirements for supporting annotation tools.

- As we provide raw data for four different registers (see below), we encourage to propose tasks that can be applied across registers, if possible. Domain-restricted tasks and models are one of the current bottlenecks in argumentation mining.

Submissions ===========

Papers should be submitted as standard workshop short papers (see http://argmining2016.arg.tech/index.php/home/call-for-papers/ ). They will be reviewed by the program committee and non-anonymous reviews will be made available at the workshop website. Papers should mention "unshared task" in their titles to distinguish themselves from other workshop submissions. We plan to organize a special poster session for unshared task papers at the workshop, followed by an open discussion. The acceptance of submissions will be based on reviewers’ assessment with focus on novelty, thoroughness, and the overall quality.

Where applicable, we encourage submission of the annotated data along with the paper as supplementary material, so that the community has a chance to explore the entire dataset rather than the few samples most likely shown in the papers.

Data ====

We provide four variants of the task across various registers.

- Variant A: Debate portals - Samples from two-sided debate portal createdebate.com

- Variant B: Debate transcripts - Two speeches (opening and closing) for both the proponent and the opponent from Intelligence squared debates (the entire debate has more participants, but the entire transcript would be extremely long for the purposes of the unshared task)

- Variant C: Opinionated news articles - Editorial articles from Room for debate from N.Y.Times

- Variant D: Discussions under opinionated articles -- Discussions from Room for debate (The IDs of the debates correspond to IDs of the articles from Variant C)

Each variant is split into three parts:

- Development set: We encourage participants to perform the exploratory analysis, task definition, annotation experiments, etc. on this set.

- Test set: This small set might serve as a benchmark for testing the annotation model (or even a computational model, for participants who go that far) and reporting agreement measures (if applicable).

- Crowdsourcing set: a bit larger set suitable for participants who plan on crowdsourcing experiments.

Note that these various splits are only a recommendation and not obligatory; participants are absolutely free to use the entire dataset if their task so requires.

The data is available at GitHub:

https://github.com/UKPLab/argmin2016-unshared-task

It can be easily forked, checked out, or downloaded as a ZIP file.

Dates =====

Deadline for all categories of submission: 8 May, 2016 at midnight Samoa time (UTC-11).

Notification of acceptance: 5 June, 2016

Camera-ready papers due: 22 June, 2016

Workshop: 12 August 2016

Same as workshop paper submissions, see http://argmining2016.arg.tech/index.php/home/call-for-papers/

Organizers ==========

Ivan Habernal, UKP Lab, TU Darmstadt Iryna Gurevych, UKP Lab, TU Darmstadt

Program Committee =================

http://argmining2016.arg.tech/index.php/home/program-committee/

References ==========

Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., & Mitchell, M. (2015). CLPsych 2015 Shared Task: Depression and PTSD on Twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (pp. 31-39). Denver, Colorado: Association for Computational Linguistics. http://www.aclweb.org/anthology/W15-1204

Smith, N. A., Cardie, C., Washington, A., & Wilkerson, J. (2014). Overview of the 2014 NLP Unshared Task in PoliInformatics. In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science (pp. 5-7). Baltimore, MD, USA: Association for Computational Linguistics. http://www.aclweb.org/anthology/W/W14/W14-2505

Zampieri, M., Tan, L., Ljubešić, N., Tiedemann, J., & Nakov, P. (2015). Overview of the DSL Shared Task 2015. In Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects (pp. 1-9). Hissar, Bulgaria: Association for Computational Linguistics. http://www.aclweb.org/anthology/W15-5401

-- ----------------------------------------------------------------------------- Ivan Habernal, Ph.D. Postdoctoral Researcher Ubiquitous Knowledge Processing (UKP) Lab FB 20 / Computer Science Department Technische Universitšt Darmstadt Hochschulstr. 10, D-64289 Darmstadt, Germany Room S2/02/B107 habernal at ukp.informatik.tu-darmstadt.de www.ukp.tu-darmstadt.de

Web Research at TU Darmstadt (WeRC) www.werc.tu-darmstadt.de

Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) GRK 1994 / www.aiphes.tu-darmstadt.de

Knowledge Discovery in Scientific Literature (KDSL) PhD program / www.kdsl.tu-darmstadt.de -----------------------------------------------------------------------------



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