IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
This work addresses emotion classification from text for natural language processing applications, but it is incremental as it combines existing methods like ELMo and BiLSTM.
The paper tackled implicit emotion classification by developing a system that achieved 2nd place out of 26 teams with a test macro F1 score of 0.710 in the WASSA 2018 IEST shared task.
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2$^{\text{nd}}$ place out of 26 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 said word vectors, 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.