TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture

Hardik Meisheri, Lipika Dey


Abstract
This paper presents system description of our submission to the SemEval-2018 task-1: Affect in tweets for the English language. We combine three different features generated using deep learning models and traditional methods in support vector machines to create a unified ensemble system. A robust representation of a tweet is learned using a multi-attention based architecture which uses a mixture of different pre-trained embeddings. In addition to this analysis of different features is also presented. Our system ranked 2nd, 5th, and 7th in different subtasks among 75 teams.
Anthology ID:
S18-1043
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:
291–299
Language:
URL:
https://www.aclweb.org/anthology/S18-1043
DOI:
10.18653/v1/S18-1043
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1043.pdf