A New Dataset for Causality Identification in Argumentative Texts

Khalid Al-Khatib; Michael Völske; Shahbaz Syed; Anh Le; Martin Potthast; Benno Stein

doi:10.18653/v1/2023.sigdial-1.31

A New Dataset for Causality Identification in Argumentative Texts

Khalid Al-Khatib, Michael Völske, Shahbaz Syed, Anh Le, Martin Potthast, Benno Stein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives). To resolve these limitations, we build the Webis-Causality-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality. The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a cost-effective manner.

Original language	English
Title of host publication	Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Editors	Svetlana Stoyanchev, Shafiq Joty, David Schlangen, Ondrej Dusek, Casey Kennington, Malihe Alikhani
Publisher	Association for Computational Linguistics, ACL Anthology
Pages	349-354
Number of pages	6
DOIs	https://doi.org/10.18653/v1/2023.sigdial-1.31
Publication status	Published - 1-Sept-2023
Event	24th Annual Meeting of the Special Interest Group on Discourse and Dialogue - Prague, Czech Republic Duration: 11-Sept-2023 → 15-Sept-2023

Conference

Conference	24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Country/Territory	Czech Republic
City	Prague
Period	11/09/2023 → 15/09/2023

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A New Dataset for Causality Identification in Argumentative TextsFinal publisher's version, 239 KBLicence: CC BY

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Al-Khatib, K., Völske, M., Syed, S., Le, A., Potthast, M., & Stein, B. (2023). A New Dataset for Causality Identification in Argumentative Texts. In S. Stoyanchev, S. Joty, D. Schlangen, O. Dusek, C. Kennington, & M. Alikhani (Eds.), Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 349-354). Association for Computational Linguistics, ACL Anthology. https://doi.org/10.18653/v1/2023.sigdial-1.31

Al-Khatib, Khalid ; Völske, Michael ; Syed, Shahbaz et al. / A New Dataset for Causality Identification in Argumentative Texts. Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. editor / Svetlana Stoyanchev ; Shafiq Joty ; David Schlangen ; Ondrej Dusek ; Casey Kennington ; Malihe Alikhani. Association for Computational Linguistics, ACL Anthology, 2023. pp. 349-354

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title = "A New Dataset for Causality Identification in Argumentative Texts",

abstract = "Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives). To resolve these limitations, we build the Webis-Causality-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality. The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a cost-effective manner.",

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Al-Khatib, K, Völske, M, Syed, S, Le, A, Potthast, M & Stein, B 2023, A New Dataset for Causality Identification in Argumentative Texts. in S Stoyanchev, S Joty, D Schlangen, O Dusek, C Kennington & M Alikhani (eds), Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, ACL Anthology, pp. 349-354, 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Prague, Czech Republic, 11/09/2023. https://doi.org/10.18653/v1/2023.sigdial-1.31

A New Dataset for Causality Identification in Argumentative Texts. / Al-Khatib, Khalid; Völske, Michael; Syed, Shahbaz et al.
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. ed. / Svetlana Stoyanchev; Shafiq Joty; David Schlangen; Ondrej Dusek; Casey Kennington; Malihe Alikhani. Association for Computational Linguistics, ACL Anthology, 2023. p. 349-354.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Völske, Michael

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AU - Potthast, Martin

AU - Stein, Benno

PY - 2023/9/1

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N2 - Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives). To resolve these limitations, we build the Webis-Causality-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality. The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a cost-effective manner.

AB - Existing datasets for causality identification in argumentative texts have several limitations, such as the type of input text (e.g., only claims), causality type (e.g., only positive), and the linguistic patterns investigated (e.g., only verb connectives). To resolve these limitations, we build the Webis-Causality-23 dataset, with sophisticated inputs (all units from arguments), a balanced distribution of causality types, and a larger number of linguistic patterns denoting causality. The dataset contains 1485 examples derived by combining the two paradigms of distant supervision and uncertainty sampling to identify diverse, high-quality samples of causality relations, and annotate them in a cost-effective manner.

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BT - Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

A2 - Stoyanchev, Svetlana

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Al-Khatib K, Völske M, Syed S, Le A, Potthast M, Stein B. A New Dataset for Causality Identification in Argumentative Texts. In Stoyanchev S, Joty S, Schlangen D, Dusek O, Kennington C, Alikhani M, editors, Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, ACL Anthology. 2023. p. 349-354 doi: 10.18653/v1/2023.sigdial-1.31

A New Dataset for Causality Identification in Argumentative Texts

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