How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

Chia‐Wei Liu, Ryan Lowe, Iulian Vlad Serban, Mike Noseworthy, Laurent Charlin, Joëlle Pineau

Abstract

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available.Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response.We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain.We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.

Locations

• arXiv (Cornell University) - View - PDF
• eScholarship@McGill (McGill) - View - PDF
• Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing - View - PDF