Building a Legal Dialogue System: Development Process, Challenges and Opportunities
This work addresses the problem of creating a legal dialogue system for users needing legal assistance, but it is incremental as it builds on existing AWS technologies and focuses on domain-specific implementation.
The paper tackles the challenge of building a domain-specific conversational agent for the legal domain by addressing issues like scope, architecture, and data preparation, and reports model accuracy on a regression test set.
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.