A Summary of the ALQAC 2021 Competition
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 22 Apr 2022.
Builds on
4 of 12 references.
References · 4
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020PhoBERT: Pre-trained language models for Vietnamesecited by 148
- 2021ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processingcited by 5
Built on by
Citing papers per year: 2022: 3; 2023: 4; 2024: 1; 2025: 0; 2026: 1.
9 papers.
- 2022Attentive Deep Neural Networks for Legal Document Retrievalcited by 13
- 2024LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domaincited by 10
- 2023Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applicationscited by 6
- 2023NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Modelscited by 2
- 2023NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichmentcited by 1
- 2023RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verificationcited by 1
- 2022Understanding Tieq Viet with Deep Learning Modelscited by 0
- 2022Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022cited by 0
- 2026Reading a Legal Question Word by Word: Embedding Trajectories of 2,144 Vietnamese Legal Headlinescited by 0
- 2026Reading a Legal Question Word by Word: Embedding Trajectories of 2,144 Vietnamese Legal Headlines8 Sep 2026
- 2024LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain2 Apr 2024
- 2023Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications22 Nov 2023
- 2023RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification16 Sep 2023
- 2023NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models16 Sep 2023
- 2023NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages through Data Enrichment11 Sep 2023
- 2022Attentive Deep Neural Networks for Legal Document Retrieval13 Dec 2022
- 2022Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 20224 Nov 2022
- 2022Understanding Tieq Viet with Deep Learning Models3 Jul 2022