Abstract
This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multi-task learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set.- Anthology ID:
- S18-1034
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 226–230
- Language:
- URL:
- https://aclanthology.org/S18-1034
- DOI:
- 10.18653/v1/S18-1034
- Cite (ACL):
- Zi-Yuan Gao and Chia-Ping Chen. 2018. deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 226–230, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets (Gao & Chen, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1034.pdf