EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification
This work addresses emotion detection in social media and entertainment dialogues, but it is incremental as it applies an existing method to a specific task.
The paper tackled emotion classification in dialogues from TV shows and Facebook chats by fine-tuning a pre-trained BERT model, achieving micro-F1 scores of 79.1% and 86.2% on two datasets and ranking 3rd out of 11 submissions.
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.