{"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/generalizing-and-hybridizing-count-based-and","title":"Generalizing and Hybridizing Count-based and Neural Language Models","arxiv_id":"1606.00499","date":"2016-06-01","proceeding":"EMNLP 2016 11","authors":["Graham Neubig","Chris Dyer"],"abstract":"Language models (LMs) are statistical models that calculate probabilities\nover sequences of words or other discrete symbols. Currently two major\nparadigms for language modeling exist: count-based n-gram models, which have\nadvantages of scalability and test-time speed, and neural LMs, which often\nachieve superior modeling performance. We demonstrate how both varieties of\nmodels can be unified in a single modeling framework that defines a set of\nprobability distributions over the vocabulary of words, and then dynamically\ncalculates mixture weights over these distributions. This formulation allows us\nto create novel hybrid models that combine the desirable features of\ncount-based and neural LMs, and experiments demonstrate the advantages of these\napproaches.","url_abs":"http://arxiv.org/abs/1606.00499v2","url_pdf":"http://arxiv.org/pdf/1606.00499v2.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":"generalizing-and-hybridizing-count-based-and","repo_url":"https://github.com/neubig/modlm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.00499","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}