KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets

Thomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, Isabelle Augenstein


Abstract
We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.
Anthology ID:
S18-1058
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:
385–389
Language:
URL:
https://aclanthology.org/S18-1058
DOI:
10.18653/v1/S18-1058
Bibkey:
Cite (ACL):
Thomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, and Isabelle Augenstein. 2018. KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 385–389, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets (Nyegaard-Signori et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1058.pdf