FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation
Chen Zhang, Luis Fernando D’Haro, Qiquan Zhang, Thomas Friedrichs, Haizhou Li
Abstract
Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation or look at a single dialogue quality dimension. One would expect a good evaluation metric to assess multiple quality dimensions at the dialogue level. To this end, we are motivated to propose a multi-dimensional dialogue-level metric, which consists of three sub-metrics with each targeting a specific dimension. The sub-metrics are trained with novel self-supervised objectives and exhibit strong correlations with human judgment for their respective dimensions. Moreover, we explore two approaches to combine the sub-metrics: metric ensemble and multitask learning. Both approaches yield a holistic metric that significantly outperforms individual sub-metrics. Compared to the existing state-of-the-art metric, the combined metrics achieve around 16% relative improvement on average across three high-quality dialogue-level evaluation benchmarks.- Anthology ID:
- 2022.emnlp-main.220
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3336–3355
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.220
- DOI:
- 10.18653/v1/2022.emnlp-main.220
- Cite (ACL):
- Chen Zhang, Luis Fernando D’Haro, Qiquan Zhang, Thomas Friedrichs, and Haizhou Li. 2022. FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3336–3355, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation (Zhang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.emnlp-main.220.pdf