{"url":"/task/morphological-inflection","name":"Morphological Inflection","slug":"morphological-inflection","description_markdown":"**Morphological Inflection** is the task of generating a target (inflected form) word from a source word (base form), given a morphological attribute, e.g. number, tense, and person etc. It is useful for alleviating data sparsity issues in translating morphologically rich languages. The transformation from a base form to an inflected form usually includes concatenating the base form with a prefix or a suffix and substituting some characters. For example, the inflected form of a Finnish stem eläkeikä (retirement age) is eläkeiittä when the case is abessive and the number is plural.\n\n\n<span class=\"description-source\">Source: [Tackling Sequence to Sequence Mapping Problems with Neural Networks ](https://arxiv.org/abs/1810.10802)</span>","categories":[{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":135,"papers_with_code":39,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[{"url":"/dataset/unimorph","name":"UniMorph 4.0","full_name":"Universal Morphology","num_papers_in_archive":9}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":39,"tagged_in_all":135,"items":[{"url":"/paper/pushing-the-limits-of-low-resource","title":"Pushing the Limits of Low-Resource Morphological Inflection","date":"2019-08-16","arxiv_id":"1908.05838","repositories_listed":4,"syntology":null},{"url":"/paper/applying-the-transformer-to-character-level","title":"Applying the Transformer to Character-level Transduction","date":"2020-05-20","arxiv_id":"2005.10213","repositories_listed":3,"syntology":{"n":5,"n_ran":2,"n_unverified":3,"n_pointer_only":2}},{"url":"/paper/an-extended-sequence-tagging-vocabulary-for","title":"An Extended Sequence Tagging Vocabulary for Grammatical Error Correction","date":"2023-02-12","arxiv_id":"2302.05913","repositories_listed":2,"syntology":null},{"url":"/paper/systematic-inequalities-in-language","title":"Systematic Inequalities in Language Technology Performance across the World's Languages","date":"2021-10-13","arxiv_id":"2110.06733","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_unverified":4,"n_pointer_only":6}},{"url":"/paper/exact-hard-monotonic-attention-for-character","title":"Exact Hard Monotonic Attention for Character-Level Transduction","date":"2019-05-15","arxiv_id":"1905.06319","repositories_listed":2,"syntology":null},{"url":"/paper/a-structured-variational-autoencoder-for","title":"A Structured Variational Autoencoder for Contextual Morphological Inflection","date":"2018-06-10","arxiv_id":"1806.03746","repositories_listed":2,"syntology":null},{"url":"/paper/can-a-neural-model-guide-fieldwork-a-case","title":"Can a Neural Model Guide Fieldwork? 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