  In this paper we present two deep-learning systems that competed at
SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble
two independent models, based on recurrent neural networks (Bi-LSTM), which
operate at the word and character level, in order to capture both the semantic
and syntactic information in tweets. Our models are augmented with a
self-attention mechanism, in order to identify the most informative words. The
embedding layer of our word-level model is initialized with word2vec word
embeddings, pretrained on a collection of 550 million English tweets. We did
not utilize any handcrafted features, lexicons or external datasets as prior
information and our models are trained end-to-end using back propagation on
constrained data. Furthermore, we provide visualizations of tweets with
annotations for the salient tokens of the attention layer that can help to
interpret the inner workings of the proposed models. We ranked 2nd out of 42
teams in Subtask A and 2nd out of 31 teams in Subtask B. However,
post-task-completion enhancements of our models achieve state-of-the-art
results ranking 1st for both subtasks.
