Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
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Abstract (arXiv)
Computational morphology has the potential to support language documentation through tasks like morphological segmentation and the generation of Interlinear Glossed Text (IGT). However, our research outputs have seen limited use in real-world language documentation settings. This position paper situates the disconnect between computational morphology and language documentation within a broader misalignment between research and practice in NLP and argues that the field risks becoming decontextualized and ineffectual without systematic integration of User-Centered Design (UCD). To demonstrate how principles from UCD can reshape the research agenda, we present a case study of GlossLM, a state-of-the-art multilingual IGT generation model. Through a small-scale user study with three documentary linguists, we find that despite strong metric based performance, the system fails to meet core usability needs in real documentation contexts. These insights raise new research questions around model constraints, label standardization, segmentation, and personalization. We argue that centering users not only produces more effective tools, but surfaces richer, more relevant research directions
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 65 references that are in the map. First posted on arXiv 12 Sep 2025.
Builds on
14 of 65 references.
References · 14
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2020Are All Languages Created Equal in Multilingual BERT?cited by 207
- 2018The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflectioncited by 41
- 2020SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflectioncited by 39
- 2022The SIGMORPHON 2022 Shared Task on Morpheme Segmentationcited by 25
- 2024Can we teach language models to gloss endangered languages?cited by 14
- 2024GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Textcited by 12
- 2024Wav2Gloss: Generating Interlinear Glossed Text from Speechcited by 8
- 2022Eeny, meeny, miny, moe. How to choose data for morphological inflectioncited by 6
- 2018Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Settingcited by 5
- 2024Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossingcited by 5
- 2024On Tables with Numbers, with Numberscited by 4
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Massively Multilingual Joint Segmentation and Glossingcited by 1
- 2026Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvancited by 0
- 2026CWoMP: Morpheme Representation Learning for Interlinear Glossingcited by 0
- 2026GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settingscited by 0
- 2026GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings3 Jun 2026
- 2026CWoMP: Morpheme Representation Learning for Interlinear Glossing18 Mar 2026
- 2026Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvan1 Mar 2026
- 2026Massively Multilingual Joint Segmentation and Glossing16 Jan 2026