IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations

Jorge Balazs, Edison Marrese-Taylor, Yutaka Matsuo


Abstract
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2nd place out of 30 teams with a test macro F1 score of 0.710. The system is composed of a single pre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context, a max-pooling operation for creating sentence representations from them, and a Dense Layer for projecting the sentence representations into label space. Our official submission was obtained by ensembling 6 of these models initialized with different random seeds. The code for replicating this paper is available at https://github.com/jabalazs/implicit_emotion.
Anthology ID:
W18-6208
Volume:
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
50–56
Language:
URL:
https://aclanthology.org/W18-6208
DOI:
10.18653/v1/W18-6208
Bibkey:
Cite (ACL):
Jorge Balazs, Edison Marrese-Taylor, and Yutaka Matsuo. 2018. IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 50–56, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations (Balazs et al., WASSA 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W18-6208.pdf
Code
 jabalazs/implicit_emotion