Abstract
Discovering new intents is of great significance for establishing the Task-Oriented Dialogue System. Most existing methods either cannot transfer prior knowledge contained in known intents or fall into the dilemma of forgetting prior knowledge in the follow-up. Furthermore, these methods do not deeply explore the intrinsic structure of unlabeled data, and as a result, cannot seek out the characteristics that define an intent in general. In this paper, starting from the intuition that discovering intents could be beneficial for identifying known intents, we propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. We adopt the Expectation Maximization framework for optimization. Specifically, In the E-step, we conduct intent discovery and explore the intrinsic structure of unlabeled data by the posterior of intent assignments. In the M-step, we alleviate the forgetting of prior knowledge transferred from known intents by optimizing the discrimination of labeled data. Extensive experiments conducted on three challenging real-world datasets demonstrate the generality and effectiveness of the proposed framework and implementation.- Anthology ID:
- 2023.acl-long.209
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3771–3784
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.209
- DOI:
- 10.18653/v1/2023.acl-long.209
- Cite (ACL):
- Yunhua Zhou, Guofeng Quan, and Xipeng Qiu. 2023. A Probabilistic Framework for Discovering New Intents. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3771–3784, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Probabilistic Framework for Discovering New Intents (Zhou et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.acl-long.209.pdf