Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022
This addresses legal information retrieval for low-resource languages, but it is incremental as it applies an existing model to a specific domain.
The paper tackled legal document retrieval and question answering in the ALQAC 2022 competition, achieving first place in retrieval and third place in QA by fine-tuning XLM-RoBERTa on limited labeled data.
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.