2021/09/01 by Mudita Sharma, Sharma, Mudita, Tony Russell‐Rose +5
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multi-Agent Systems and Negotiation #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2109.00381
openalex publication_date 2021/09/01 · openalex created_date 2021/09/13 · openalex updated_date 2026/07/28
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.