# Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022

Source: [Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022](<https://www.sota2.com/research/paper/68fa6ddf1e209131cea3c973>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:56.138Z

Authors: Hieu Nguyen Van, Dat Nguyen, Phuong Minh Nguyen, Minh Le Nguyen

Published: 2022-11-04T00:50:20.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.02200>)

[PDF](<https://arxiv.org/pdf/2211.02200.pdf>)