{"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/neural-morphology-dataset-and-models-for","title":"Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered","arxiv_id":"2105.12428","date":"2021-05-26","proceeding":"NoDaLiDa 2021 5","authors":["Mika Hämäläinen","Niko Partanen","Jack Rueter","Khalid Alnajjar"],"abstract":"We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.","url_abs":"https://arxiv.org/abs/2105.12428v1","url_pdf":"https://arxiv.org/pdf/2105.12428v1.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":"neural-morphology-dataset-and-models-for","repo_url":"https://github.com/mikahama/uralicNLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"lemmatization","task_name":"Lemmatization"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}