Unsupervised Learning of Hierarchical Conversation Structure
Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, Mari Ostendorf
Abstract
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization.- Anthology ID:
- 2022.findings-emnlp.415
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5657–5670
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.415
- DOI:
- Cite (ACL):
- Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, and Mari Ostendorf. 2022. Unsupervised Learning of Hierarchical Conversation Structure. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5657–5670, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Learning of Hierarchical Conversation Structure (Lu et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.findings-emnlp.415.pdf