{"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/input-to-output-gate-to-improve-rnn-language","title":"Input-to-Output Gate to Improve RNN Language Models","arxiv_id":"1709.08907","date":"2017-09-26","proceeding":"IJCNLP 2017 11","authors":["Sho Takase","Jun Suzuki","Masaaki Nagata"],"abstract":"This paper proposes a reinforcing method that refines the output layers of\nexisting Recurrent Neural Network (RNN) language models. We refer to our\nproposed method as Input-to-Output Gate (IOG). IOG has an extremely simple\nstructure, and thus, can be easily combined with any RNN language models. Our\nexperiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG\nconsistently boosts the performance of several different types of current\ntopline RNN language models.","url_abs":"http://arxiv.org/abs/1709.08907v2","url_pdf":"http://arxiv.org/pdf/1709.08907v2.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":"input-to-output-gate-to-improve-rnn-language","repo_url":"https://github.com/nttcslab-nlp/iog","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"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}