# Building a Legal Dialogue System: Development Process, Challenges and Opportunities

arXiv: https://arxiv.org/abs/2109.00381 · HTML: https://mlsanity.com/paper/2109.00381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [A Survey of Available Corpora for Building Data-Driven Dialogue Systems](https://mlsanity.com/paper/1512.05742/) (2015)

## Built on by (most cited first, 1 of 1)

- [Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents](https://mlsanity.com/paper/2605.14057/) (2026)

## Abstract (arXiv)

This paper presents key principles and solutions to the challenges faced in designing a domain-specific conversational agent for the legal domain. It includes issues of scope, platform, architecture and preparation of input data. It provides functionality in answering user queries and recording user information including contact details and case-related information. It utilises deep learning technology built upon Amazon Web Services (AWS) LEX in combination with AWS Lambda. Due to lack of publicly available data, we identified two methods including crowdsourcing experiments and archived enquiries to develop a number of linguistic resources. This includes a training dataset, set of predetermined responses for the conversational agent, a set of regression test cases and a further conversation test set. We propose a hierarchical bot structure that facilitates multi-level delegation and report model accuracy on the regression test set. Additionally, we highlight features that are added to the bot to improve the conversation flow and overall user experience.