@inproceedings{yu-etal-2018-improving,
title = "Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network",
author = "Yu, Jianfei and
Marujo, Lu{\'\i}s and
Jiang, Jing and
Karuturi, Pradeep and
Brendel, William",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1137",
doi = "10.18653/v1/D18-1137",
pages = "1097--1102",
abstract = "In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.",
}
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<abstract>In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.</abstract>
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%0 Conference Proceedings
%T Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
%A Yu, Jianfei
%A Marujo, Luís
%A Jiang, Jing
%A Karuturi, Pradeep
%A Brendel, William
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yu-etal-2018-improving
%X In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.
%R 10.18653/v1/D18-1137
%U https://aclanthology.org/D18-1137
%U https://doi.org/10.18653/v1/D18-1137
%P 1097-1102
Markdown (Informal)
[Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network](https://aclanthology.org/D18-1137) (Yu et al., EMNLP 2018)
ACL