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