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
- 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:
- 291–299
- Language:
- URL:
- https://aclanthology.org/S18-1043
- DOI:
- 10.18653/v1/S18-1043
- Cite (ACL):
- Hardik Meisheri and Lipika Dey. 2018. TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 291–299, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture (Meisheri & Dey, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S18-1043.pdf