SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets

Venkatesh Duppada, Royal Jain, Sushant Hiray


Abstract
The paper describes the best performing system for the SemEval-2018 Affect in Tweets(English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1stposition out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2% to 76.4 %, thus, pushing the state-of-the-art significantly.
Anthology ID:
S18-1002
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:
18–23
Language:
URL:
https://www.aclweb.org/anthology/S18-1002
DOI:
10.18653/v1/S18-1002
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1002.pdf