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        <title>Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need</title>
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    <name type="personal">
        <namePart type="given">Shounak</namePart>
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    <name type="personal">
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    <abstract>Automatic Charge Identification (ACI) is the task of identifying the relevant charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Existing works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset (\textless 3%) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baselines for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.88</identifier>
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%0 Conference Proceedings
%T Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need
%A Paul, Shounak
%A Goyal, Pawan
%A Ghosh, Saptarshi
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F paul-etal-2020-automatic
%X Automatic Charge Identification (ACI) is the task of identifying the relevant charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Existing works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset (\textless 3%) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baselines for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.
%R 10.18653/v1/2020.coling-main.88
%U https://aclanthology.org/2020.coling-main.88
%U https://doi.org/10.18653/v1/2020.coling-main.88
%P 1011-1022

Markdown (Informal)

[Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need](https://aclanthology.org/2020.coling-main.88) (Paul et al., COLING 2020)

• Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need (Paul et al., COLING 2020)

ACL

• Shounak Paul, Pawan Goyal, and Saptarshi Ghosh. 2020. Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1011–1022, Barcelona, Spain (Online). International Committee on Computational Linguistics.