SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
Information
Title
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
Authors
Sanghwan Bae, Jihun Choi, Sang-goo Lee
Year
2019 / 6
Keywords
natural language processing, class distribution mismatch, conversational networks
Publication Type
International Workshop
Publication
International Workshop on Semantic Evaluation (SemEval 2019)
Link
url
Abstract
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.