# A Survey on Legal Question Answering Systems

arXiv: https://arxiv.org/abs/2110.07333 · HTML: https://mlsanity.com/paper/2110.07333/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 125 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://mlsanity.com/paper/1911.00841/) (2019)
- [A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](https://mlsanity.com/paper/2005.05257/) (2020)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Neural Abstractive Summarization with Structural Attention](https://mlsanity.com/paper/2004.09739/) (2020)
- [Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network](https://mlsanity.com/paper/1703.05320/) (2017)
- [Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions](https://mlsanity.com/paper/1905.13350/) (2019)
- [Analysis of Watson's Strategies for Playing Jeopardy!](https://mlsanity.com/paper/1402.0571/) (2014)

## Built on by (most cited first, 10 of 10)

- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models](https://mlsanity.com/paper/2309.14735/) (2023)
- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements](https://mlsanity.com/paper/2502.07022/) (2025)
- [AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments](https://mlsanity.com/paper/2603.04718/) (2026)
- [Exploring the State of the Art in Legal QA Systems](https://mlsanity.com/paper/2304.06623/) (2023)
- [Efficient Learning Content Retrieval with Knowledge Injection](https://mlsanity.com/paper/2412.00125/) (2024)
- [ViHERMES: A Graph-Grounded Multihop Question Answering Benchmark and System for Vietnamese Healthcare Regulations](https://mlsanity.com/paper/2602.07361/) (2026)

## Abstract (arXiv)

Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities. The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.