# A Summary of the ALQAC 2021 Competition

arXiv: https://arxiv.org/abs/2204.10717 · HTML: https://mlsanity.com/paper/2204.10717/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing](https://mlsanity.com/paper/2106.13403/) (2021)

## Built on by (most cited first, 9 of 9)

- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications](https://mlsanity.com/paper/2311.13095/) (2023)
- [NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models](https://mlsanity.com/paper/2309.09070/) (2023)
- [NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment](https://mlsanity.com/paper/2309.05500/) (2023)
- [RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification](https://mlsanity.com/paper/2309.09071/) (2023)
- [Understanding Tieq Viet with Deep Learning Models](https://mlsanity.com/paper/2207.00975/) (2022)
- [Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022](https://mlsanity.com/paper/2211.02200/) (2022)
- [Reading a Legal Question Word by Word: Embedding Trajectories of 2,144 Vietnamese Legal Headlines](https://mlsanity.com/paper/2609.08372/) (2026)

## Abstract (arXiv)

We summarize the evaluation of the first Automated Legal Question Answering Competition (ALQAC 2021). The competition this year contains three tasks, which aims at processing the statute law document, which are Legal Text Information Retrieval (Task 1), Legal Text Entailment Prediction (Task 2), and Legal Text Question Answering (Task 3). The final goal of these tasks is to build a system that can automatically determine whether a particular statement is lawful. There is no limit to the approaches of the participating teams. This year, there are 5 teams participating in Task 1, 6 teams participating in Task 2, and 5 teams participating in Task 3. There are in total 36 runs submitted to the organizer. In this paper, we summarize each team's approaches, official results, and some discussion about the competition. Only results of the teams who successfully submit their approach description paper are reported in this paper.