Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols
It addresses the lack of standardized evaluation in conversational AI, which hinders fair comparisons and understanding of system values, but is incremental as it builds on existing methods.
This paper synthesizes automated and human evaluation methods for dialogue systems by surveying 20 recent papers and comparing them against expert evaluations on Alexa Prize 2020 data, aiming to identify shortcomings and effective dimensions for more robust evaluation protocols.
As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult to conduct fair comparative studies across different approaches and gain an insightful understanding of their values. To foster this research, a more robust evaluation protocol must be set in place. This paper presents a comprehensive synthesis of both automated and human evaluation methods on dialogue systems, identifying their shortcomings while accumulating evidence towards the most effective evaluation dimensions. A total of 20 papers from the last two years are surveyed to analyze three types of evaluation protocols: automated, static, and interactive. Finally, the evaluation dimensions used in these papers are compared against our expert evaluation on the system-user dialogue data collected from the Alexa Prize 2020.