{"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/syllable-aware-neural-language-models-a","title":"Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones","arxiv_id":"1707.06480","date":"2017-07-20","proceeding":"EMNLP 2017 9","authors":["Zhenisbek Assylbekov","Rustem Takhanov","Bagdat Myrzakhmetov","Jonathan N. Washington"],"abstract":"Syllabification does not seem to improve word-level RNN language modeling\nquality when compared to character-based segmentation. However, our best\nsyllable-aware language model, achieving performance comparable to the\ncompetitive character-aware model, has 18%-33% fewer parameters and is trained\n1.2-2.2 times faster.","url_abs":"http://arxiv.org/abs/1707.06480v1","url_pdf":"http://arxiv.org/pdf/1707.06480v1.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":"syllable-aware-neural-language-models-a","repo_url":"https://github.com/zh3nis/lstm-syl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}