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

November 04, 2022 ยท Declared Dead ยท ๐Ÿ› International Conference on Knowledge and Systems Engineering

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Authors Hieu Nguyen Van, Dat Nguyen, Phuong Minh Nguyen, Minh Le Nguyen arXiv ID 2211.02200 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 10 Venue International Conference on Knowledge and Systems Engineering Last Checked 5 months ago
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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