deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

Zi-Yuan Gao, Chia-Ping Chen


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S18-1034.pdf