Seq2Emo: A sequence to multi-label emotion classification model
Proceedings of the 2021 conference of the North American chapter of …, 2021•aclanthology.org
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.
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
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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