{"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/morphnet-a-sequence-to-sequence-model-that","title":"Morphological analysis using a sequence decoder","arxiv_id":"1805.07946","date":"2018-05-21","proceeding":"TACL 2019 3","authors":["Ekin Akyürek","Erenay Dayanik","Deniz Yuret"],"abstract":"We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and outperform whole-tag models. In addition, generating morphological features as a sequence rather than e.g.\\ an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the art results in nine languages of different morphological complexity under low-resource, high-resource and transfer learning settings. We also introduce TrMor2018, a new high accuracy Turkish morphology dataset. Our Morse implementation and the TrMor2018 dataset are available online to support future research\\footnote{See \\url{https://github.com/ai-ku/Morse.jl} for a Morse implementation in Julia/Knet \\cite{knet2016mlsys} and \\url{https://github.com/ai-ku/TrMor2018} for the new Turkish dataset.}.","url_abs":"https://arxiv.org/abs/1805.07946v2","url_pdf":"https://arxiv.org/pdf/1805.07946v2.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":"morphnet-a-sequence-to-sequence-model-that","repo_url":"https://github.com/ai-ku/Morse.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"morphnet-a-sequence-to-sequence-model-that","repo_url":"https://github.com/ai-ku/TrMor2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"lemma","task_name":"LEMMA"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"trmor2018","name":"TrMor2018","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}