Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers

• URL: http://arxiv.org/abs/2304.14447v1
• Date: Thu, 27 Apr 2023 18:19:24 GMT
• Title: Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers
• Authors: Nguyen Anh Tu, Hoang Thi Thu Uyen, Tu Minh Phuong, Ngo Xuan Bach
• Abstract summary: We propose using deep neural networks to extract important information from Vietnamese legal questions. Given a legal question in natural language, the goal is to extract all the segments that contain the needed information to answer the question.
• Score: 3.116035935327534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose using deep neural networks to extract important information from Vietnamese legal questions, a fundamental task towards building a question answering system in the legal domain. Given a legal question in natural language, the goal is to extract all the segments that contain the needed information to answer the question. We introduce a deep model that solves the task in three stages. First, our model leverages recent advanced autoencoding language models to produce contextual word embeddings, which are then combined with character-level and POS-tag information to form word representations. Next, bidirectional long short-term memory networks are employed to capture the relations among words and generate sentence-level representations. At the third stage, borrowing ideas from graph-based dependency parsing methods which provide a global view on the input sentence, we use biaffine classifiers to estimate the probability of each pair of start-end words to be an important segment. Experimental results on a public Vietnamese legal dataset show that our model outperforms the previous work by a large margin, achieving 94.79% in the F1 score. The results also prove the effectiveness of using contextual features extracted from pre-trained language models combined with other types of features such as character-level and POS-tag features when training on a limited dataset.

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