{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ntua-slp-at-semeval-2018-task-2-predicting","title":"NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention","arxiv_id":"1804.06657","date":"2018-04-18","proceeding":"SEMEVAL 2018 6","authors":["Christos Baziotis","Nikos Athanasiou","Georgios Paraskevopoulos","Nikolaos Ellinas","Athanasia Kolovou","Alexandros Potamianos"],"abstract":"In this paper we present a deep-learning model that competed at SemEval-2018\nTask 2 \"Multilingual Emoji Prediction\". We participated in subtask A, in which\nwe are called to predict the most likely associated emoji in English tweets.\nThe proposed architecture relies on a Long Short-Term Memory network, augmented\nwith an attention mechanism, that conditions the weight of each word, on a\n\"context vector\" which is taken as the aggregation of a tweet's meaning.\nMoreover, we initialize the embedding layer of our model, with word2vec word\nembeddings, pretrained on a dataset of 550 million English tweets. Finally, our\nmodel does not rely on hand-crafted features or lexicons and is trained\nend-to-end with back-propagation. We ranked 2nd out of 48 teams.","url_abs":"http://arxiv.org/abs/1804.06657v1","url_pdf":"http://arxiv.org/pdf/1804.06657v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ntua-slp-at-semeval-2018-task-2-predicting","repo_url":"https://github.com/FengJiaChunFromSYSU/ntua-slp-semeval2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ntua-slp-at-semeval-2018-task-2-predicting","repo_url":"https://github.com/alexandra-chron/ntua-slp-semeval2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ntua-slp-at-semeval-2018-task-2-predicting","repo_url":"https://github.com/cbaziotis/ntua-slp-semeval2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"task-2","task_name":"Task 2"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.06657","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}