NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource
  Languages through Data Enrichment
Hai-Long Nguyen Dieu-Quynh Nguyen Hoang-Trung Nguyen Thu-Trang Pham Huu-Dong Nguyen Thach-Anh Nguyen Thi-Hai-Yen Vuong Ha-Thanh Nguyen
In recent years, natural language processing has gained significant
popularity in various sectors, including the legal domain. This paper presents
NeCo Team's solutions to the Vietnamese text processing tasks provided in the
Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on
legal domain knowledge acquisition for low-resource languages through data
enrichment. Our methods for the legal document retrieval task employ a
combination of similarity ranking and deep learning models, while for the
second task, which requires extracting an answer from a relevant legal article
in response to a question, we propose a range of adaptive techniques to handle
different question types. Our approaches achieve outstanding results on both
tasks of the competition, demonstrating the potential benefits and
effectiveness of question answering systems in the legal field, particularly
for low-resource languages.