A Dynamic Speaker Model for Conversational Interactions

Hao Cheng, Hao Fang, Mari Ostendorf


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N19-1284.pdf
Code
 hao-cheng/dynamic_speaker_model