Answering Legal Questions by Learning Neural Attentive Text Representation

Phi Manh Kien, Ha-Thanh Nguyen, Ngo Xuan Bach, Vu Tran, Minh Le Nguyen, Tu Minh Phuong


Abstract
Text representation plays a vital role in retrieval-based question answering, especially in the legal domain where documents are usually long and complicated. The better the question and the legal documents are represented, the more accurate they are matched. In this paper, we focus on the task of answering legal questions at the article level. Given a legal question, the goal is to retrieve all the correct and valid legal articles, that can be used as the basic to answer the question. We present a retrieval-based model for the task by learning neural attentive text representation. Our text representation method first leverages convolutional neural networks to extract important information in a question and legal articles. Attention mechanisms are then used to represent the question and articles and select appropriate information to align them in a matching process. Experimental results on an annotated corpus consisting of 5,922 Vietnamese legal questions show that our model outperforms state-of-the-art retrieval-based methods for question answering by large margins in terms of both recall and NDCG.
Anthology ID:
2020.coling-main.86
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
988–998
Language:
URL:
https://aclanthology.org/2020.coling-main.86
DOI:
10.18653/v1/2020.coling-main.86
Bibkey:
Cite (ACL):
Phi Manh Kien, Ha-Thanh Nguyen, Ngo Xuan Bach, Vu Tran, Minh Le Nguyen, and Tu Minh Phuong. 2020. Answering Legal Questions by Learning Neural Attentive Text Representation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 988–998, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Answering Legal Questions by Learning Neural Attentive Text Representation (Kien et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.86.pdf