%0 Conference Proceedings
%T deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets
%A Gao, Zi-Yuan
%A Chen, Chia-Ping
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F gao-chen-2018-deepsa2018
%X 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.
%R 10.18653/v1/S18-1034
%U https://aclanthology.org/S18-1034
%U https://doi.org/10.18653/v1/S18-1034
%P 226-230