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    <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.</abstract>
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%0 Conference Proceedings
%T SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
%A Duppada, Venkatesh
%A Jain, Royal
%A Hiray, Sushant
%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 duppada-etal-2018-seernet
%X 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.
%R 10.18653/v1/S18-1002
%U https://aclanthology.org/S18-1002/
%U https://doi.org/10.18653/v1/S18-1002
%P 18-23

Markdown (Informal)

[SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets](https://aclanthology.org/S18-1002/) (Duppada et al., SemEval 2018)

• SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets (Duppada et al., SemEval 2018)

ACL

• Venkatesh Duppada, Royal Jain, and Sushant Hiray. 2018. SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 18–23, New Orleans, Louisiana. Association for Computational Linguistics.