NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention

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Authors Alexandros Potamianos, Nikos Athanasiou, Athanasia Kolovou, Georgios Paraskevopoulos, Christos Baziotis, Nikolaos Ellinas
Journal/Conference Name SEMEVAL 2018 6
Paper Category
Paper Abstract In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict the most likely associated emoji in English tweets. The proposed architecture relies on a Long Short-Term Memory network, augmented with an attention mechanism, that conditions the weight of each word, on a "context vector" which is taken as the aggregation of a tweet's meaning. Moreover, we initialize the embedding layer of our model, with word2vec word embeddings, pretrained on a dataset of 550 million English tweets. Finally, our model does not rely on hand-crafted features or lexicons and is trained end-to-end with back-propagation. We ranked 2nd out of 48 teams.
Date of publication 2018
Code Programming Language Python

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