@inproceedings{li-etal-2018-co,
title = "A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness",
author = "Li, Xiangju and
Song, Kaisong and
Feng, Shi and
Wang, Daling and
Zhang, Yifei",
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/D18-1506",
doi = "10.18653/v1/D18-1506",
pages = "4752--4757",
abstract = "Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.",
}
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<abstract>Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.</abstract>
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%0 Conference Proceedings
%T A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness
%A Li, Xiangju
%A Song, Kaisong
%A Feng, Shi
%A Wang, Daling
%A Zhang, Yifei
%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 li-etal-2018-co
%X Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.
%R 10.18653/v1/D18-1506
%U https://aclanthology.org/D18-1506
%U https://doi.org/10.18653/v1/D18-1506
%P 4752-4757
Markdown (Informal)
[A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness](https://aclanthology.org/D18-1506) (Li et al., EMNLP 2018)
ACL