{"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/deep-contextualized-word-representations-for","title":"Deep contextualized word representations for detecting sarcasm and irony","arxiv_id":"1809.09795","date":"2018-09-26","proceeding":"WS 2018 10","authors":["Suzana Ilić","Edison Marrese-Taylor","Jorge A. Balazs","Yutaka Matsuo"],"abstract":"Predicting context-dependent and non-literal utterances like sarcastic and\nironic expressions still remains a challenging task in NLP, as it goes beyond\nlinguistic patterns, encompassing common sense and shared knowledge as crucial\ncomponents. To capture complex morpho-syntactic features that can usually serve\nas indicators for irony or sarcasm across dynamic contexts, we propose a model\nthat uses character-level vector representations of words, based on ELMo. We\ntest our model on 7 different datasets derived from 3 different data sources,\nproviding state-of-the-art performance in 6 of them, and otherwise offering\ncompetitive results.","url_abs":"http://arxiv.org/abs/1809.09795v1","url_pdf":"http://arxiv.org/pdf/1809.09795v1.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":"deep-contextualized-word-representations-for","repo_url":"https://github.com/epochx/elmo4irony","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"}],"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":"https://app.syntology.ai/?focus=1809.09795","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}