  Question answering (QA) in law is a challenging problem because legal
documents are much more complicated than normal texts in terms of terminology,
structure, and temporal and logical relationships. It is even more difficult to
perform legal QA for low-resource languages like Vietnamese where labeled data
are rare and pre-trained language models are still limited. In this paper, we
try to overcome these limitations by implementing a Vietnamese article-level
retrieval-based legal QA system and introduce a novel method to improve the
performance of language models by improving data quality through weak labeling.
Our hypothesis is that in contexts where labeled data are limited, efficient
data enrichment can help increase overall performance. Our experiments are
designed to test multiple aspects, which demonstrate the effectiveness of the
proposed technique.
