{"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-1-predicting","title":"NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning","arxiv_id":"1804.06658","date":"2018-04-18","proceeding":"SEMEVAL 2018 6","authors":["Christos Baziotis","Nikos Athanasiou","Alexandra Chronopoulou","Athanasia Kolovou","Georgios Paraskevopoulos","Nikolaos Ellinas","Shrikanth Narayanan","Alexandros Potamianos"],"abstract":"In this paper we present deep-learning models that submitted to the\nSemEval-2018 Task~1 competition: \"Affect in Tweets\". We participated in all\nsubtasks for English tweets. We propose a Bi-LSTM architecture equipped with a\nmulti-layer self attention mechanism. The attention mechanism improves the\nmodel performance and allows us to identify salient words in tweets, as well as\ngain insight into the models making them more interpretable. Our model utilizes\na set of word2vec word embeddings trained on a large collection of 550 million\nTwitter messages, augmented by a set of word affective features. Due to the\nlimited amount of task-specific training data, we opted for a transfer learning\napproach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A.\nThe proposed approach ranked 1st in Subtask E \"Multi-Label Emotion\nClassification\", 2nd in Subtask A \"Emotion Intensity Regression\" and achieved\ncompetitive results in other subtasks.","url_abs":"http://arxiv.org/abs/1804.06658v1","url_pdf":"http://arxiv.org/pdf/1804.06658v1.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-1-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-1-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-1-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":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"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=1804.06658","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}