{"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/unsupervised-neural-hidden-markov-models","title":"Unsupervised Neural Hidden Markov Models","arxiv_id":"1609.09007","date":"2016-09-28","proceeding":"WS 2016 11","authors":["Ke Tran","Yonatan Bisk","Ashish Vaswani","Daniel Marcu","Kevin Knight"],"abstract":"In this work, we present the first results for neuralizing an Unsupervised\nHidden Markov Model. We evaluate our approach on tag in- duction. Our approach\noutperforms existing generative models and is competitive with the\nstate-of-the-art though with a simpler model easily extended to include\nadditional context.","url_abs":"http://arxiv.org/abs/1609.09007v1","url_pdf":"http://arxiv.org/pdf/1609.09007v1.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":"unsupervised-neural-hidden-markov-models","repo_url":"https://github.com/ketranm/neuralHMM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"unsupervised-neural-hidden-markov-models","repo_url":"https://github.com/WinnieHAN/dnhmm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.09007","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}