{"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-iest-2018-ensemble-of-neural","title":"NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification","arxiv_id":"1809.00717","date":"2018-09-03","proceeding":"WS 2018 10","authors":["Alexandra Chronopoulou","Aikaterini Margatina","Christos Baziotis","Alexandros Potamianos"],"abstract":"In this paper we present our approach to tackle the Implicit Emotion Shared\nTask (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from\nwhich a certain word has been removed, we are asked to predict the emotion of\nthe missing word. In this work, we experiment with neural Transfer Learning\n(TL) methods. Our models are based on LSTM networks, augmented with a\nself-attention mechanism. We use the weights of various pretrained models, for\ninitializing specific layers of our networks. We leverage a big collection of\nunlabeled Twitter messages, for pretraining word2vec word embeddings and a set\nof diverse language models. Moreover, we utilize a sentiment analysis dataset\nfor pretraining a model, which encodes emotion related information. The\nsubmitted model consists of an ensemble of the aforementioned TL models. Our\nteam ranked 3rd out of 30 participants, achieving an F1 score of 0.703.","url_abs":"http://arxiv.org/abs/1809.00717v1","url_pdf":"http://arxiv.org/pdf/1809.00717v1.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-iest-2018-ensemble-of-neural","repo_url":"https://github.com/alexandra-chron/wassa-2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}