Seq2Emo: A Sequence to Multi-Label Emotion Classification Model

Chenyang Huang, Amine Trabelsi, Xuebin Qin, Nawshad Farruque, Lili Mou, Osmar Zaïane


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.
Anthology ID:
2021.naacl-main.375
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4717–4724
Language:
URL:
https://aclanthology.org/2021.naacl-main.375
DOI:
10.18653/v1/2021.naacl-main.375
Bibkey:
Cite (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.
Cite (Informal):
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model (Huang et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.375.pdf
Video:
 https://aclanthology.org/2021.naacl-main.375.mp4
Data
GoEmotions