{"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/iiidyt-at-iest-2018-implicit-emotion","title":"IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations","arxiv_id":"1808.08672","date":"2018-08-27","proceeding":"WS 2018 10","authors":["Jorge A. Balazs","Edison Marrese-Taylor","Yutaka Matsuo"],"abstract":"In this paper we describe our system designed for the WASSA 2018 Implicit\nEmotion Shared Task (IEST), which obtained 2$^{\\text{nd}}$ place out of 26\nteams with a test macro F1 score of $0.710$. The system is composed of a single\npre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory\nNetwork BiLSTM for enriching word representations with context, a max-pooling\noperation for creating sentence representations from said word vectors, and a\nDense Layer for projecting the sentence representations into label space. Our\nofficial submission was obtained by ensembling 6 of these models initialized\nwith different random seeds. The code for replicating this paper is available\nat https://github.com/jabalazs/implicit_emotion.","url_abs":"http://arxiv.org/abs/1808.08672v2","url_pdf":"http://arxiv.org/pdf/1808.08672v2.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":"iiidyt-at-iest-2018-implicit-emotion","repo_url":"https://github.com/jabalazs/implicit_emotion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"elmo","method_name":"ELMo"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}