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

Christos Baziotis, Athanasiou Nikolaos, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, Alexandros Potamianos


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.
Anthology ID:
S18-1069
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
438–444
Language:
URL:
https://www.aclweb.org/anthology/S18-1069
DOI:
10.18653/v1/S18-1069
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1069.pdf