@inproceedings{kim-lee-2019-decay,
title = "Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding",
author = "Kim, Jonggu and
Lee, Jong-Hyeok",
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/N19-1372",
doi = "10.18653/v1/N19-1372",
pages = "3718--3726",
abstract = "To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function. We also propose a method to identify and label the current speaker to improve the SLU accuracy. In experiments on the benchmark dataset used in Dialog State Tracking Challenge 4, the proposed models achieved significantly higher F1 scores than the state-of-the-art contextual models. Finally, we analyze the effectiveness of the introduced models in detail. The analysis demonstrates that the proposed methods were effective to improve SLU accuracy individually.",
}
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%0 Conference Proceedings
%T Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding
%A Kim, Jonggu
%A Lee, Jong-Hyeok
%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 kim-lee-2019-decay
%X To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function. We also propose a method to identify and label the current speaker to improve the SLU accuracy. In experiments on the benchmark dataset used in Dialog State Tracking Challenge 4, the proposed models achieved significantly higher F1 scores than the state-of-the-art contextual models. Finally, we analyze the effectiveness of the introduced models in detail. The analysis demonstrates that the proposed methods were effective to improve SLU accuracy individually.
%R 10.18653/v1/N19-1372
%U https://aclanthology.org/N19-1372
%U https://doi.org/10.18653/v1/N19-1372
%P 3718-3726
Markdown (Informal)
[Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding](https://aclanthology.org/N19-1372) (Kim & Lee, NAACL 2019)
ACL