@inproceedings{masumura-etal-2018-neural,
title = "Neural Dialogue Context Online End-of-Turn Detection",
author = "Masumura, Ryo and
Tanaka, Tomohiro and
Ando, Atsushi and
Ishii, Ryo and
Higashinaka, Ryuichiro and
Aono, Yushi",
editor = "Komatani, Kazunori and
Litman, Diane and
Yu, Kai and
Papangelis, Alex and
Cavedon, Lawrence and
Nakano, Mikio",
booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5024",
doi = "10.18653/v1/W18-5024",
pages = "224--228",
abstract = "This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker{'}s and collocutor{'}s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker{'}s acoustic sequential features and collocutor{'}s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker{'}s utterances and collocutor{'}s utterances into consideration.",
}
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%0 Conference Proceedings
%T Neural Dialogue Context Online End-of-Turn Detection
%A Masumura, Ryo
%A Tanaka, Tomohiro
%A Ando, Atsushi
%A Ishii, Ryo
%A Higashinaka, Ryuichiro
%A Aono, Yushi
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F masumura-etal-2018-neural
%X This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker’s utterances and collocutor’s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker’s and collocutor’s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker’s acoustic sequential features and collocutor’s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker’s utterances and collocutor’s utterances into consideration.
%R 10.18653/v1/W18-5024
%U https://aclanthology.org/W18-5024
%U https://doi.org/10.18653/v1/W18-5024
%P 224-228
Markdown (Informal)
[Neural Dialogue Context Online End-of-Turn Detection](https://aclanthology.org/W18-5024) (Masumura et al., SIGDIAL 2018)
ACL
- Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, and Yushi Aono. 2018. Neural Dialogue Context Online End-of-Turn Detection. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 224–228, Melbourne, Australia. Association for Computational Linguistics.