Answering Legal Questions by Learning Neural Attentive Text Representation

Proceedings of the 28th International Conference on Computational Linguistics, pages 988–998 Barcelona, Spain (Online), December 8-13, 2020 988 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† †Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam ‡Japan Advanced Institute of Science and Technology kienpm2205@gmail.com {bachnx,phuongtm}@ptit.edu.vn {nguyenhathanh,vu.tran,nguyenml}@jaist.ac.jp 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. 1 Introduction Question answering in the legal domain is a difficult task due to the complication and topic variety of legal document systems. Giving an accurate answer to a legal question usually requires domain expert knowledge, making this task more challenging even with human beings. Retrieval-based approaches, which return relevant legal documents respect to the input question, are often feasible and more ap- propriate. Legal documents, however, are usually long and complicated, which takes time to read and locate exact information. Relevant legal articles are, therefore, the expected result of retrieval-based legal question answering systems, where users are easier to find a useful answer than the whole documents. Similar to an information retrieval system, a typical retrieval-based question answering one usually consists of three main steps: question1 representation, document representation, and matching, which judges the relevance between the question and documents (Jurafsky and Martin, 2009). Text representa- tion, i.e., question and document representation, therefore, plays a crucial role in such systems. Good text representation methods are expected to be able to extract important information from the input question and documents and be able to select appropriate information to align them in the matching step. Recently, many successful text representation methods have been proposed thanks to the advance- ment of deep neural network models. The most popular deep neural architectures for text representation include convolutional neural networks (CNNs) (Kim, 2014; Shen et al., 2014; Severyn and Moschitti, 2015; Vaswani et al., 2017), recurrent neural networks (RNNs) (Mikolov et al., 2011), and their varia- tions such as long short-term memories (LSTMs) (Wang et al., 2016; Palangi et …

Content truncated to 500 words