Emotion detection in text is an important task in NLP and is essential in
many applications. Most of the existing methods treat this task as a problem of
single-label multi-class text classification. To predict multiple emotions for
one instance, most of the existing works regard it as a general Multi-label
Classification (MLC) problem, where they usually either apply a manually
determined threshold on the last output layer of their neural network models or
train multiple binary classifiers and make predictions in the fashion of
one-vs-all. However, compared to labels in the general MLC datasets, the number
of emotion categories are much fewer (less than 10). Additionally, emotions
tend to have more correlations with each other. For example, the human usually
does not express "joy" and "anger" at the same time, but it is very likely to
have "joy" and "love" expressed together. Given this intuition, in this paper,
we propose a Latent Variable Chain (LVC) transformation and a tailored model --
Seq2Emo model that not only naturally predicts multiple emotion labels but also
takes into consideration their correlations. We perform the experiments on the
existing multi-label emotion datasets as well as on our newly collected
datasets. The results show that our model compares favorably with existing
state-of-the-art methods.

本文提出了一个 Latent Variable Chain (LVC) 转换和 Seq2Emo 模型，它不仅可以自然地预测多重情感标签，还考虑了它们之间的相关性，从而取得了比现有最先进方法更好的结果。