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        <title>Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions</title>
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        <namePart type="given">Hai</namePart>
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    <name type="personal">
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    <name type="personal">
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    <abstract>In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
%A Ye, Hai
%A Jiang, Xin
%A Luo, Zhunchen
%A Chao, Wenhan
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ye-etal-2018-interpretable
%X In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.
%R 10.18653/v1/N18-1168
%U https://aclanthology.org/N18-1168
%U https://doi.org/10.18653/v1/N18-1168
%P 1854-1864

Markdown (Informal)

[Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://aclanthology.org/N18-1168) (Ye et al., NAACL 2018)

• Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (Ye et al., NAACL 2018)

ACL

• Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018. Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1854–1864, New Orleans, Louisiana. Association for Computational Linguistics.