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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2054.pdf
- Code
- baaesh/semeval19_task3
- Data
- EmoContext