mlsanityData to 14 Oct 2026

Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation

arXiv 2509.10644 ↗First posted 12 Sep 2025cs.CLcited by 4

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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

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2020Are All Languages Created Equal in Multilingual BERT?cited by 207
  3. 2018The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflectioncited by 41
  4. 2020SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflectioncited by 39
  5. 2022The SIGMORPHON 2022 Shared Task on Morpheme Segmentationcited by 25
  6. 2024Can we teach language models to gloss endangered languages?cited by 14
  7. 2024GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Textcited by 12
  8. 2024Wav2Gloss: Generating Interlinear Glossed Text from Speechcited by 8
  9. 2022Eeny, meeny, miny, moe. How to choose data for morphological inflectioncited by 6
  10. 2018Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Settingcited by 5
  11. 2024Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossingcited by 5
  12. 2024On Tables with Numbers, with Numberscited by 4
2 more
  1. 2022Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languagescited by 2
  2. 2022A Major Obstacle for NLP Research: Let's Talk about Time Allocation!cited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.