Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment

Understand

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.