Abstract
Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limited number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.- Anthology ID:
- 2020.acl-main.126
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1363–1374
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.126
- DOI:
- 10.18653/v1/2020.acl-main.126
- Cite (ACL):
- Weixin Liang, James Zou, and Zhou Yu. 2020. Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1363–1374, Online. Association for Computational Linguistics.
- Cite (Informal):
- Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (Liang et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.126.pdf
- Code
- Weixin-Liang/dialog_evaluation_CMADE