Neural Dialogue Context Online End-of-Turn Detection
Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono
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.- Anthology ID:
- W18-5024
- Volume:
- Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 224–228
- Language:
- URL:
- https://aclanthology.org/W18-5024
- DOI:
- 10.18653/v1/W18-5024
- Cite (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.
- Cite (Informal):
- Neural Dialogue Context Online End-of-Turn Detection (Masumura et al., SIGDIAL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5024.pdf