  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.
