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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
226–230
Language:
URL:
https://www.aclweb.org/anthology/S18-1034
DOI:
10.18653/v1/S18-1034
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1034.pdf