SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification

Sanghwan Bae, Jihun Choi, Sang-goo Lee


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.
Anthology ID:
S19-2054
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
312–317
Language:
URL:
https://aclanthology.org/S19-2054
DOI:
10.18653/v1/S19-2054
Bibkey:
Cite (ACL):
Sanghwan Bae, Jihun Choi, and Sang-goo Lee. 2019. SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 312–317, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification (Bae et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2054.pdf
Code
 baaesh/semeval19_task3
Data
EmoContext