@inproceedings{cheng-etal-2019-dynamic,
title = "A Dynamic Speaker Model for Conversational Interactions",
author = "Cheng, Hao and
Fang, Hao and
Ostendorf, Mari",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1284",
doi = "10.18653/v1/N19-1284",
pages = "2772--2785",
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.",
}
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%0 Conference Proceedings
%T A Dynamic Speaker Model for Conversational Interactions
%A Cheng, Hao
%A Fang, Hao
%A Ostendorf, Mari
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cheng-etal-2019-dynamic
%X 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.
%R 10.18653/v1/N19-1284
%U https://aclanthology.org/N19-1284
%U https://doi.org/10.18653/v1/N19-1284
%P 2772-2785
Markdown (Informal)
[A Dynamic Speaker Model for Conversational Interactions](https://aclanthology.org/N19-1284) (Cheng et al., NAACL 2019)
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.