DynaEval: Unifying Turn and Dialogue Level Evaluation
Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li
Abstract
A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.- Anthology ID:
- 2021.acl-long.441
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5676–5689
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.441
- DOI:
- 10.18653/v1/2021.acl-long.441
- Cite (ACL):
- Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, and Haizhou Li. 2021. DynaEval: Unifying Turn and Dialogue Level Evaluation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5676–5689, Online. Association for Computational Linguistics.
- Cite (Informal):
- DynaEval: Unifying Turn and Dialogue Level Evaluation (Zhang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.acl-long.441.pdf
- Code
- e0397123/DynaEval
- Data
- ConvAI2, DailyDialog, EmpatheticDialogues, FED