Abstract
Individual differences in speakers are reflected in their language use as well as in their interests and opinions. Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations. In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context. Initial model training is unsupervised, using context-sensitive language generation as an objective, with the context being the conversation history. Further fine-tuning can leverage task-dependent supervised training. The learned neural representation of speakers is shown to be useful for content ranking in a socialbot and dialog act prediction in human-human conversations.- Anthology ID:
- N19-1284
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2772–2785
- Language:
- URL:
- https://aclanthology.org/N19-1284
- DOI:
- 10.18653/v1/N19-1284
- Cite (ACL):
- Hao Cheng, Hao Fang, and Mari Ostendorf. 2019. A Dynamic Speaker Model for Conversational Interactions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2772–2785, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- A Dynamic Speaker Model for Conversational Interactions (Cheng et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/N19-1284.pdf
- Code
- hao-cheng/dynamic_speaker_model