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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CITATION STYLE
Huang, C., Trabelsi, A., Qin, X., Farruque, N., Mou, L., & Zaïane, O. (2021). Seq2Emo: A Sequence to Multi-Label Emotion Classification Model. In NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 4717–4724). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.naacl-main.375
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