@inproceedings{saadat-yazdi-etal-2023-uncovering,
title = "Uncovering Implicit Inferences for Improved Relational Argument Mining",
author = "Saadat-Yazdi, Ameer and
Pan, Jeff Z. and
Kokciyan, Nadin",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.182",
doi = "10.18653/v1/2023.eacl-main.182",
pages = "2484--2495",
abstract = "Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may be related to each other via implicit inferences. This task often requires external commonsense knowledge to discover how one argument relates to another. State-of-the-art methods, however, rely on pre-defined knowledge graphs, and thus might not cover target argument pairs well. We introduce a new generative neuro-symbolic approach to finding inference chains that connect the argument pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2-5{\%} in F1 score, on all three datasets.",
}
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<abstract>Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may be related to each other via implicit inferences. This task often requires external commonsense knowledge to discover how one argument relates to another. State-of-the-art methods, however, rely on pre-defined knowledge graphs, and thus might not cover target argument pairs well. We introduce a new generative neuro-symbolic approach to finding inference chains that connect the argument pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2-5% in F1 score, on all three datasets.</abstract>
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%0 Conference Proceedings
%T Uncovering Implicit Inferences for Improved Relational Argument Mining
%A Saadat-Yazdi, Ameer
%A Pan, Jeff Z.
%A Kokciyan, Nadin
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F saadat-yazdi-etal-2023-uncovering
%X Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may be related to each other via implicit inferences. This task often requires external commonsense knowledge to discover how one argument relates to another. State-of-the-art methods, however, rely on pre-defined knowledge graphs, and thus might not cover target argument pairs well. We introduce a new generative neuro-symbolic approach to finding inference chains that connect the argument pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2-5% in F1 score, on all three datasets.
%R 10.18653/v1/2023.eacl-main.182
%U https://aclanthology.org/2023.eacl-main.182
%U https://doi.org/10.18653/v1/2023.eacl-main.182
%P 2484-2495
Markdown (Informal)
[Uncovering Implicit Inferences for Improved Relational Argument Mining](https://aclanthology.org/2023.eacl-main.182) (Saadat-Yazdi et al., EACL 2023)
ACL