EmotionX-KU: BERT-Max based Contextual Emotion Classifier

Understand

We propose a contextual emotion classifier based on a transferable language model and dynamic max pooling, which predicts the emotion of each utterance in a dialogue.

• A representative emotion analysis task, EmotionX, requires to consider contextual information from colloquial dialogues and to deal with a class imbalance problem.
• To alleviate these problems, our model leverages the self-attention based transferable language model and the weighted cross entropy loss.
• Furthermore, we apply post-training and fine-tuning mechanisms to enhance the domain adaptability of our model and utilize several machine learning techniques to improve its performance.