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    <titleInfo>
        <title>Argumentation-Driven Evidence Association in Criminal Cases</title>
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    <name type="personal">
        <namePart type="given">WenHan</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.257</identifier>
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%0 Conference Proceedings
%T Argumentation-Driven Evidence Association in Criminal Cases
%A Teng, Yefei
%A Chao, WenHan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F teng-chao-2021-argumentation-driven
%X Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.
%R 10.18653/v1/2021.findings-emnlp.257
%U https://aclanthology.org/2021.findings-emnlp.257
%U https://doi.org/10.18653/v1/2021.findings-emnlp.257
%P 2997-3001

Markdown (Informal)

[Argumentation-Driven Evidence Association in Criminal Cases](https://aclanthology.org/2021.findings-emnlp.257) (Teng & Chao, Findings 2021)

• Argumentation-Driven Evidence Association in Criminal Cases (Teng & Chao, Findings 2021)

ACL

• Yefei Teng and WenHan Chao. 2021. Argumentation-Driven Evidence Association in Criminal Cases. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2997–3001, Punta Cana, Dominican Republic. Association for Computational Linguistics.