Abstract
Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically. We then denoise the weakly labeled data using the Shapley algorithm. Finally, we use the denoised data to train a user engagement detector. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.- Anthology ID:
- 2021.acl-long.283
- 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
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3652–3665
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.283
- DOI:
- 10.18653/v1/2021.acl-long.283
- Cite (ACL):
- Weixin Liang, Kai-Hui Liang, and Zhou Yu. 2021. HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations. 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 3652–3665, Online. Association for Computational Linguistics.
- Cite (Informal):
- HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations (Liang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.283.pdf
- Code
- Weixin-Liang/HERALD
- Data
- ConvAI2