@inproceedings{kato-etal-2017-utterance,
title = "Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders",
author = "Kato, Tsuneo and
Nagai, Atsushi and
Noda, Naoki and
Sumitomo, Ryosuke and
Wu, Jianming and
Yamamoto, Seiichi",
editor = "Jokinen, Kristiina and
Stede, Manfred and
DeVault, David and
Louis, Annie",
booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
month = aug,
year = "2017",
address = {Saarbr{\"u}cken, Germany},
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5508",
doi = "10.18653/v1/W17-5508",
pages = "60--64",
abstract = "Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonlinear operation on the vectors of child nodes, the operation is considered to be different intrinsically depending on types of child nodes. To relax the difference, a data-driven untying of autoencoders (AEs) is proposed. The experimental result of the utterance intent classification showed an improved accuracy with the proposed method compared with the basic tied RAE and untied RAE based on a manual rule.",
}
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%0 Conference Proceedings
%T Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders
%A Kato, Tsuneo
%A Nagai, Atsushi
%A Noda, Naoki
%A Sumitomo, Ryosuke
%A Wu, Jianming
%A Yamamoto, Seiichi
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F kato-etal-2017-utterance
%X Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonlinear operation on the vectors of child nodes, the operation is considered to be different intrinsically depending on types of child nodes. To relax the difference, a data-driven untying of autoencoders (AEs) is proposed. The experimental result of the utterance intent classification showed an improved accuracy with the proposed method compared with the basic tied RAE and untied RAE based on a manual rule.
%R 10.18653/v1/W17-5508
%U https://aclanthology.org/W17-5508
%U https://doi.org/10.18653/v1/W17-5508
%P 60-64
Markdown (Informal)
[Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders](https://aclanthology.org/W17-5508) (Kato et al., SIGDIAL 2017)
ACL