EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text

Nikhil Chakravartula, Vijayasaradhi Indurthi


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S19-2033.pdf