In legal text processing and reasoning, one normally performs information retrieval to find relevant documents of an input question, and then performs textual entailment to answer the question. The former is about relevancy whereas the latter is about affirmation (or conclusion). While relevancy and affirmation are two different concepts, there is obviously a connection between them. That is why performing retrieval and textual entailment sequentially and independently may not make the most of this mutually supportive relationship. This paper, therefore, propose a multi–task learning model for these two tasks to improve their performance. Technically, in the COLIEE dataset, we use the information of Task 4 (conclusions) to improve the performance of Task 3 (searching for legal provisions related to the question). Our empirical findings indicate that this supportive relationship truly exists. This important insight sheds light on how leveraging relationship between tasks can significantly enhance the effectiveness of our multi-task learning approach for legal text processing.

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.