@inproceedings{al-khatib-etal-2023-new,
title = "A New Dataset for Causality Identification in Argumentative Texts",
author = "Al Khatib, Khalid and
Voelske, Michael and
Le, Anh and
Syed, Shahbaz and
Potthast, Martin and
Stein, Benno",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.31",
doi = "10.18653/v1/2023.sigdial-1.31",
pages = "349--354",
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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%0 Conference Proceedings
%T A New Dataset for Causality Identification in Argumentative Texts
%A Al Khatib, Khalid
%A Voelske, Michael
%A Le, Anh
%A Syed, Shahbaz
%A Potthast, Martin
%A Stein, Benno
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F al-khatib-etal-2023-new
%X 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.
%R 10.18653/v1/2023.sigdial-1.31
%U https://aclanthology.org/2023.sigdial-1.31
%U https://doi.org/10.18653/v1/2023.sigdial-1.31
%P 349-354
Markdown (Informal)
[A New Dataset for Causality Identification in Argumentative Texts](https://aclanthology.org/2023.sigdial-1.31) (Al Khatib et al., SIGDIAL 2023)
ACL
- Khalid Al Khatib, Michael Voelske, Anh Le, Shahbaz Syed, Martin Potthast, and Benno Stein. 2023. A New Dataset for Causality Identification in Argumentative Texts. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 349–354, Prague, Czechia. Association for Computational Linguistics.