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.- Anthology ID:
- N18-1168
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1854–1864
- Language:
- URL:
- https://aclanthology.org/N18-1168
- DOI:
- 10.18653/v1/N18-1168
- Cite (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.
- Cite (Informal):
- Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (Ye et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-1168.pdf
- Code
- oceanypt/Court-View-Gen