Ngo Xuan Bach

Also published as: Ngo Xuan Bach


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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
Proceedings of the 28th International Conference on Computational Linguistics

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.


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A Reranking Model for Discourse Segmentation using Subtree Features
Ngo Xuan Bach | Nguyen Le Minh | Akira Shimazu
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue


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Supervised and Semi-Supervised Sequence Learning for Recognition of Requisite Part and Effectuation Part in Law Sentences
Le-Minh Nguyen | Ngo Xuan Bach | Akira Shimazu
Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing

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Learning Logical Structures of Paragraphs in Legal Articles
Ngo Xuan Bach | Nguyen Le Minh | Tran Thi Oanh | Akira Shimazu
Proceedings of 5th International Joint Conference on Natural Language Processing