{"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/beyond-characters-subword-level-morpheme","title":"Beyond Characters: Subword-level Morpheme Segmentation","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL (SIGMORPHON) 2022 7","authors":["Ben Peters","Andre F. T. Martins"],"abstract":"This paper presents DeepSPIN’s submissions to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation. We make three submissions, all to the word-level subtask. First, we show that entmax-based sparse sequence-tosequence models deliver large improvements over conventional softmax-based models, echoing results from other tasks. Then, we challenge the assumption that models for morphological tasks should be trained at the character level by building a transformer that generates morphemes as sequences of unigram language model-induced subwords. This subword transformer outperforms all of our character-level models and wins the word-level subtask. Although we do not submit an official submission to the sentence-level subtask, we show that this subword-based approach is highly effective there as well.","url_abs":"https://aclanthology.org/2022.sigmorphon-1.14","url_pdf":"https://aclanthology.org/2022.sigmorphon-1.14.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":[],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"morpheme-segmentaiton","task_name":"Morpheme Segmentaiton"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/morpheme-segmentaiton-on-unimorph-4-0","task":"Morpheme Segmentaiton","dataset":"UniMorph 4.0","model":"Subword-ULM transformer (DeepSPIN-3; soft-attention, 1-5 entmax)","rank_in_archive_order":1,"of":19,"metrics":{"macro avg (subtask 1)":"97.29"},"uses_additional_data":false},{"leaderboard":"/sota/morpheme-segmentaiton-on-unimorph-4-0","task":"Morpheme Segmentaiton","dataset":"UniMorph 4.0","model":"Char LSTM (DeepSPIN-2; soft-attention, 1-5 entmax)","rank_in_archive_order":2,"of":19,"metrics":{"macro avg (subtask 1)":"97.15"},"uses_additional_data":false},{"leaderboard":"/sota/morpheme-segmentaiton-on-unimorph-4-0","task":"Morpheme Segmentaiton","dataset":"UniMorph 4.0","model":"Char LSTM (DeepSPIN-1; soft-attention)","rank_in_archive_order":4,"of":19,"metrics":{"macro avg (subtask 1)":"96.32"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}