@inproceedings{huang-etal-2021-seq2emo,
title = "{S}eq2{E}mo: A Sequence to Multi-Label Emotion Classification Model",
author = {Huang, Chenyang and
Trabelsi, Amine and
Qin, Xuebin and
Farruque, Nawshad and
Mou, Lili and
Za{\"\i}ane, Osmar},
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.375",
doi = "10.18653/v1/2021.naacl-main.375",
pages = "4717--4724",
abstract = "Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval{'}18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.",
}
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<abstract>Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval’18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.</abstract>
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%0 Conference Proceedings
%T Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
%A Huang, Chenyang
%A Trabelsi, Amine
%A Qin, Xuebin
%A Farruque, Nawshad
%A Mou, Lili
%A Zaïane, Osmar
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F huang-etal-2021-seq2emo
%X Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval’18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.
%R 10.18653/v1/2021.naacl-main.375
%U https://aclanthology.org/2021.naacl-main.375
%U https://doi.org/10.18653/v1/2021.naacl-main.375
%P 4717-4724
Markdown (Informal)
[Seq2Emo: A Sequence to Multi-Label Emotion Classification Model](https://aclanthology.org/2021.naacl-main.375) (Huang et al., NAACL 2021)
ACL
- Chenyang Huang, Amine Trabelsi, Xuebin Qin, Nawshad Farruque, Lili Mou, and Osmar Zaïane. 2021. Seq2Emo: A Sequence to Multi-Label Emotion Classification Model. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4717–4724, Online. Association for Computational Linguistics.