@inproceedings{meisheri-dey-2018-tcs,
title = "{TCS} Research at {S}em{E}val-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture",
author = "Meisheri, Hardik and
Dey, Lipika",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1043/",
doi = "10.18653/v1/S18-1043",
pages = "291--299",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture
%A Meisheri, Hardik
%A Dey, Lipika
%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 meisheri-dey-2018-tcs
%X 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.
%R 10.18653/v1/S18-1043
%U https://aclanthology.org/S18-1043/
%U https://doi.org/10.18653/v1/S18-1043
%P 291-299
Markdown (Informal)
[TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture](https://aclanthology.org/S18-1043/) (Meisheri & Dey, SemEval 2018)
ACL