{"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/nmt-keras-a-very-flexible-toolkit-with-a","title":"NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning","arxiv_id":"1807.03096","date":"2018-07-09","proceeding":null,"authors":["Álvaro Peris","Francisco Casacuberta"],"abstract":"We present NMT-Keras, a flexible toolkit for training deep learning models,\nwhich puts a particular emphasis on the development of advanced applications of\nneural machine translation systems, such as interactive-predictive translation\nprotocols and long-term adaptation of the translation system via continuous\nlearning. NMT-Keras is based on an extended version of the popular Keras\nlibrary, and it runs on Theano and Tensorflow. State-of-the-art neural machine\ntranslation models are deployed and used following the high-level framework\nprovided by Keras. Given its high modularity and flexibility, it also has been\nextended to tackle different problems, such as image and video captioning,\nsentence classification and visual question answering.","url_abs":"http://arxiv.org/abs/1807.03096v3","url_pdf":"http://arxiv.org/pdf/1807.03096v3.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":"nmt-keras-a-very-flexible-toolkit-with-a","repo_url":"https://github.com/lvapeab/nmt-keras","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"nmt","task_name":"NMT"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-classification","task_name":"Sentence Classification"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"video-captioning","task_name":"Video Captioning"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"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}