论文标题
Miko团队：ALQAC 2022中的法律问题回答的深度学习方法
Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022
论文作者
论文摘要
我们介绍了有效的基于深度学习的方法，用于法律文档处理，包括在自动化法律问题答案竞争（ALQAC 2022）中回答任务的法律文件检索和法律问题。在这场比赛中，我们将1 \ textsuperscript {st}放置在第一个任务中，而3 \ textsuperscript {rd}放置在第二个任务中。我们的方法基于XLM-Roberta模型，该模型在对特定任务进行微调之前，先于大量未标记的语料库进行预训练。实验结果表明，我们的方法在具有有限标记的数据的法律检索信息任务中很好地工作。此外，此方法可以应用于低资源语言的其他信息检索任务。
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.
