تقرير
Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers
| العنوان: | Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers |
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| المؤلفون: | Tu, Nguyen Anh, Uyen, Hoang Thi Thu, Phuong, Tu Minh, Bach, Ngo Xuan |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | 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. Comment: accepted as the oral presentation at ICONIP 2021 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-92270-2_44 |
| URL الوصول: | http://arxiv.org/abs/2304.14447 |
| رقم الأكسشن: | edsarx.2304.14447 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-92270-2_44 |
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