Abstract
This paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context of a text conversation. Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pre-trained on a large collection of Twitter data. In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task.- Anthology ID:
- S19-2033
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 205–209
- Language:
- URL:
- https://aclanthology.org/S19-2033
- DOI:
- 10.18653/v1/S19-2033
- Cite (ACL):
- Nikhil Chakravartula and Vijayasaradhi Indurthi. 2019. EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 205–209, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text (Chakravartula & Indurthi, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/S19-2033.pdf