@inproceedings{lin-etal-2021-contextual,
title = "Contextual Domain Classification with Temporal Representations",
author = "Lin, Tzu-Hsiang and
Shi, Yipeng and
Ye, Chentao and
Fan, Yang and
Ruan, Weitong and
Barut, Emre and
Hamza, Wael and
Su, Chengwei",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.6",
doi = "10.18653/v1/2021.naacl-industry.6",
pages = "41--48",
abstract = "In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. Experiments on the Contextual Domain Classification (CDC) task with various encoder architectures show that temporal representations combining both information outperforms only one of the two. We further demonstrate that our contextual Transformer is able to reduce 13.04{\%} of classification errors compared to a non-contextual baseline. We also conduct empirical analyses to study recent versus distant context and opportunities to lower deployment costs.",
}
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<abstract>In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. Experiments on the Contextual Domain Classification (CDC) task with various encoder architectures show that temporal representations combining both information outperforms only one of the two. We further demonstrate that our contextual Transformer is able to reduce 13.04% of classification errors compared to a non-contextual baseline. We also conduct empirical analyses to study recent versus distant context and opportunities to lower deployment costs.</abstract>
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%0 Conference Proceedings
%T Contextual Domain Classification with Temporal Representations
%A Lin, Tzu-Hsiang
%A Shi, Yipeng
%A Ye, Chentao
%A Fan, Yang
%A Ruan, Weitong
%A Barut, Emre
%A Hamza, Wael
%A Su, Chengwei
%Y Kim, Young-bum
%Y Li, Yunyao
%Y Rambow, Owen
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F lin-etal-2021-contextual
%X In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. Experiments on the Contextual Domain Classification (CDC) task with various encoder architectures show that temporal representations combining both information outperforms only one of the two. We further demonstrate that our contextual Transformer is able to reduce 13.04% of classification errors compared to a non-contextual baseline. We also conduct empirical analyses to study recent versus distant context and opportunities to lower deployment costs.
%R 10.18653/v1/2021.naacl-industry.6
%U https://aclanthology.org/2021.naacl-industry.6
%U https://doi.org/10.18653/v1/2021.naacl-industry.6
%P 41-48
Markdown (Informal)
[Contextual Domain Classification with Temporal Representations](https://aclanthology.org/2021.naacl-industry.6) (Lin et al., NAACL 2021)
ACL
- Tzu-Hsiang Lin, Yipeng Shi, Chentao Ye, Yang Fan, Weitong Ruan, Emre Barut, Wael Hamza, and Chengwei Su. 2021. Contextual Domain Classification with Temporal Representations. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 41–48, Online. Association for Computational Linguistics.