@inproceedings{wang-etal-2020-integrating,
title = "Integrating User History into Heterogeneous Graph for Dialogue Act Recognition",
author = "Wang, Dong and
Li, Ziran and
Zheng, Haitao and
Shen, Ying",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.372",
doi = "10.18653/v1/2020.coling-main.372",
pages = "4211--4221",
abstract = "Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user{'}s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. To handle the noise caused by introducing the user{'}s historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process. We evaluate the proposed method on two benchmark datasets MSDialog and MRDA. The experimental results verify the effectiveness of integrating user{'}s historical answers, and show that our proposed model outperforms the state-of-the-art methods.",
}
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<abstract>Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user’s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. To handle the noise caused by introducing the user’s historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process. We evaluate the proposed method on two benchmark datasets MSDialog and MRDA. The experimental results verify the effectiveness of integrating user’s historical answers, and show that our proposed model outperforms the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Integrating User History into Heterogeneous Graph for Dialogue Act Recognition
%A Wang, Dong
%A Li, Ziran
%A Zheng, Haitao
%A Shen, Ying
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F wang-etal-2020-integrating
%X Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user’s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. To handle the noise caused by introducing the user’s historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process. We evaluate the proposed method on two benchmark datasets MSDialog and MRDA. The experimental results verify the effectiveness of integrating user’s historical answers, and show that our proposed model outperforms the state-of-the-art methods.
%R 10.18653/v1/2020.coling-main.372
%U https://aclanthology.org/2020.coling-main.372
%U https://doi.org/10.18653/v1/2020.coling-main.372
%P 4211-4221
Markdown (Informal)
[Integrating User History into Heterogeneous Graph for Dialogue Act Recognition](https://aclanthology.org/2020.coling-main.372) (Wang et al., COLING 2020)
ACL