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        <title>End-to-end Argument Mining with Cross-corpora Multi-task Learning</title>
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    <name type="personal">
        <namePart type="given">Hiroaki</namePart>
        <namePart type="family">Ozaki</namePart>
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    <name type="personal">
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    <abstract>Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00481</identifier>
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        <date>2022</date>
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%0 Journal Article
%T End-to-end Argument Mining with Cross-corpora Multi-task Learning
%A Morio, Gaku
%A Ozaki, Hiroaki
%A Morishita, Terufumi
%A Yanai, Kohsuke
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F morio-etal-2022-end
%X Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.
%R 10.1162/tacl_a_00481
%U https://aclanthology.org/2022.tacl-1.37
%U https://doi.org/10.1162/tacl_a_00481
%P 639-658

Markdown (Informal)

[End-to-end Argument Mining with Cross-corpora Multi-task Learning](https://aclanthology.org/2022.tacl-1.37) (Morio et al., TACL 2022)

• End-to-end Argument Mining with Cross-corpora Multi-task Learning (Morio et al., TACL 2022)

ACL

• Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, and Kohsuke Yanai. 2022. End-to-end Argument Mining with Cross-corpora Multi-task Learning. Transactions of the Association for Computational Linguistics, 10:639–658.