SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
This work addresses a specific data mismatch problem in conversational emotion classification, with incremental improvements.
The paper tackled the class distribution mismatch between training and test data in the SemEval 2019 Contextual Emotion Detection task by extending imbalance methods and proposing a novel neural architecture, achieving a micro F1 score of 0.766.
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation.