Xuebin Qin


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Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Chenyang Huang | Amine Trabelsi | Xuebin Qin | Nawshad Farruque | Lili Mou | Osmar Zaïane
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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.