This paper describes our approach to the EmotionX-2019, the shared task of
SocialNLP 2019. To detect emotion for each utterance of two datasets from the
TV show Friends and Facebook chat log EmotionPush, we propose two-step deep
learning based methodology: (i) encode each of the utterance into a sequence of
vectors that represent its meaning; and (ii) use a simply softmax classifier to
predict one of the emotions amongst four candidates that an utterance may
carry. Notice that the source of labeled utterances is not rich, we utilise a
well-trained model, known as BERT, to transfer part of the knowledge learned
from a large amount of corpus to our model. We then focus on fine-tuning our
model until it well fits to the in-domain data. The performance of the proposed
model is evaluated by micro-F1 scores, i.e., 79.1% and 86.2% for the testsets
of Friends and EmotionPush, respectively. Our model ranks 3rd among 11
submissions.

本文介绍了我们通过深度学习和使用 BERT 模型的方法，在 EmotionX-2019 和 SocialNLP 2019 的共享任务中，对来自电视节目 Friends 和 Facebook 聊天记录 EmotionPush 的每个话语的情感进行了识别，并通过微平均 F1 分数对模型性能进行了评估，得到了同类中的第三名。