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

TL;DR

This paper details the development of a legal dialogue system using deep learning and AWS, addressing challenges like data scarcity and proposing a hierarchical structure for improved user interaction.

Contribution

It introduces a novel legal dialogue system built with AWS LEX, including methods for data collection and a hierarchical bot architecture for better conversation management.

Findings

Achieved high accuracy on regression test cases

Developed a new dataset through crowdsourcing and archived data

Enhanced user experience with feature improvements

Abstract

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.