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In this paper we present our approach to tackle the Implicit Emotion SharedTask (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, fromwhich a certain word has been removed, we are asked to predict the emotion ofthe missing word. In this work, we experiment with neural Transfer Learning(TL) methods. Our models are based on LSTM networks, augmented with aself-attention mechanism. We use the weights of various pretrained models, forinitializing specific layers of our networks. We leverage a big collection ofunlabeled Twitter messages, for pretraining word2vec word embeddings and a setof diverse language models. Moreover, we utilize a sentiment analysis datasetfor pretraining a model, which encodes emotion related information. Thesubmitted model consists of an ensemble of the aforementioned TL models. Ourteam ranked 3rd out of 30 participants, achieving an F1 score of 0.703.

NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification