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://preview.aclanthology.org/add_missing_videos/2021.naacl-main.375/
- DOI:
- 10.18653/v1/2021.naacl-main.375
- 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)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2021.naacl-main.375.pdf
- Data
- GoEmotions