@inproceedings{chen-etal-2018-joint,
    title = "Joint Learning for Emotion Classification and Emotion Cause Detection",
    author = "Chen, Ying  and
      Hou, Wenjun  and
      Cheng, Xiyao  and
      Li, Shoushan",
    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/D18-1066/",
    doi = "10.18653/v1/D18-1066",
    pages = "646--651",
    abstract = "We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising."
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        <namePart type="given">Ying</namePart>
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    <abstract>We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1066</identifier>
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%0 Conference Proceedings
%T Joint Learning for Emotion Classification and Emotion Cause Detection
%A Chen, Ying
%A Hou, Wenjun
%A Cheng, Xiyao
%A Li, Shoushan
%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 chen-etal-2018-joint
%X We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.
%R 10.18653/v1/D18-1066
%U https://aclanthology.org/D18-1066/
%U https://doi.org/10.18653/v1/D18-1066
%P 646-651
Markdown (Informal)
[Joint Learning for Emotion Classification and Emotion Cause Detection](https://aclanthology.org/D18-1066/) (Chen et al., EMNLP 2018)
ACL