@inproceedings{saxena-etal-2018-emotionx,
title = "{E}motion{X}-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling",
author = "Saxena, Rohit and
Bhat, Savita and
Pedanekar, Niranjan",
editor = "Ku, Lun-Wei and
Li, Cheng-Te",
booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3509/",
doi = "10.18653/v1/W18-3509",
pages = "50--55",
abstract = "This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38{\%} on Friends test dataset and 56.73{\%} on EmotionPush test dataset. We report an improvement of 22.51{\%} in Friends dataset and 36.04{\%} in EmotionPush dataset over baseline results."
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%0 Conference Proceedings
%T EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
%A Saxena, Rohit
%A Bhat, Savita
%A Pedanekar, Niranjan
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F saxena-etal-2018-emotionx
%X This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38% on Friends test dataset and 56.73% on EmotionPush test dataset. We report an improvement of 22.51% in Friends dataset and 36.04% in EmotionPush dataset over baseline results.
%R 10.18653/v1/W18-3509
%U https://aclanthology.org/W18-3509/
%U https://doi.org/10.18653/v1/W18-3509
%P 50-55
Markdown (Informal)
[EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling](https://aclanthology.org/W18-3509/) (Saxena et al., SocialNLP 2018)
ACL