Seq2Emo: A Sequence to Multi-Label Emotion Classification Model

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

NAACL 2021long44 citations

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.

BibTeX

@inproceedings{huang-etal-2021-seq2emo,
    title = "{S}eq2{E}mo: A Sequence to Multi-Label Emotion Classification Model",
    author = {Huang, Chenyang  and
      Trabelsi, Amine  and
      Qin, Xuebin  and
      Farruque, Nawshad  and
      Mou, Lili  and
      Za{\"i}ane, Osmar},
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.375/",
    doi = "10.18653/v1/2021.naacl-main.375",
    pages = "4717--4724"
}