Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022

• URL: http://arxiv.org/abs/2211.02200v1
• Date: Fri, 4 Nov 2022 00:50:20 GMT
• Title: Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022
• Authors: Hieu Nguyen Van, Dat Nguyen, Phuong Minh Nguyen and Minh Le Nguyen
• Abstract summary: We introduce efficient deep learning-based methods for legal document processing in the Automated Legal Question Answering Competition (ALQAC 2022) Our method is based on the XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus before fine-tuning to the specific tasks. The experimental results showed that our method works well in legal retrieval information tasks with limited labeled data.
• Score: 2.242125769416219
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In this competition, we achieve 1\textsuperscript{st} place in the first task and 3\textsuperscript{rd} place in the second task. Our method is based on the XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus before fine-tuning to the specific tasks. The experimental results showed that our method works well in legal retrieval information tasks with limited labeled data. Besides, this method can be applied to other information retrieval tasks in low-resource languages.

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