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.
翻译:我们引入了高效的深层次学习处理法律文件的方法,包括法律文件检索和法律问题回答在自动法律问题回答竞争(ALQAC 2022)中的任务。在这种竞争中,我们在第一项任务中占据了1\textoverscript{st}的位置,在第二项任务中占据了3\textsuperscript{rd}的位置。我们的方法以XLM-ROBERTA模式为基础,该模式在微调具体任务之前先从大量未标记的文具中培训出来。实验结果显示,我们的方法在法律检索信息任务中效果良好,但标签数据有限。此外,这一方法还可以用于其他低资源语言的信息检索任务。