%0 Conference Proceedings %T NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention %A Baziotis, Christos %A Nikolaos, Athanasiou %A Kolovou, Athanasia %A Paraskevopoulos, Georgios %A Ellinas, Nikolaos %A Potamianos, Alexandros %Y Apidianaki, Marianna %Y Mohammad, Saif M. %Y May, Jonathan %Y Shutova, Ekaterina %Y Bethard, Steven %Y Carpuat, Marine %S Proceedings of the 12th International Workshop on Semantic Evaluation %D 2018 %8 June %I Association for Computational Linguistics %C New Orleans, Louisiana %F baziotis-etal-2018-ntua-slp %X 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. %R 10.18653/v1/S18-1069 %U https://aclanthology.org/S18-1069 %U https://doi.org/10.18653/v1/S18-1069 %P 438-444