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

arXiv 2211.02200 ↗First posted 4 Nov 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

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 1textsuperscriptst place in the first task and 3textsuperscriptrd 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 4 Nov 2022.

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References · 13

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  1. 2022A Summary of the ALQAC 2021 Competitioncited by 9

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