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In this paper we present deep-learning models that submitted to theSemEval-2018 Task~1 competition: "Affect in Tweets". We participated in allsubtasks for English tweets. We propose a Bi-LSTM architecture equipped with amulti-layer self attention mechanism. The attention mechanism improves themodel performance and allows us to identify salient words in tweets, as well asgain insight into the models making them more interpretable. Our model utilizesa set of word2vec word embeddings trained on a large collection of 550 millionTwitter messages, augmented by a set of word affective features. Due to thelimited amount of task-specific training data, we opted for a transfer learningapproach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A.The proposed approach ranked 1st in Subtask E "Multi-Label EmotionClassification", 2nd in Subtask A "Emotion Intensity Regression" and achievedcompetitive results in other subtasks.

NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning