2020
ACL
ACL 2020
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
Abstract
AbstractThe lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Interdisciplinary and Natural Language Processing
🐣
Hot Topic Early Bird
- automatic evaluation
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Generation > Language Modeling
Interdisciplinary > Linguistics > Computational Linguistics
Artificial Intelligence > Core AI > Dialogue Systems
Natural Language Processing > Applications > Natural Language Generation