Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.
Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language.
This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue.
Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence.