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

arXiv: https://arxiv.org/abs/2509.10644 · HTML: https://mlsanity.com/paper/2509.10644/

## 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 (most cited first, 14 of 14)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2006.11572/) (2020)
- [The SIGMORPHON 2022 Shared Task on Morpheme Segmentation](https://mlsanity.com/paper/2206.07615/) (2022)
- [Can we teach language models to gloss endangered languages?](https://mlsanity.com/paper/2406.18895/) (2024)
- [GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text](https://mlsanity.com/paper/2403.06399/) (2024)
- [Wav2Gloss: Generating Interlinear Glossed Text from Speech](https://mlsanity.com/paper/2403.13169/) (2024)
- [Eeny, meeny, miny, moe. How to choose data for morphological inflection](https://mlsanity.com/paper/2210.14465/) (2022)
- [Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting](https://mlsanity.com/paper/1809.08733/) (2018)
- [Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://mlsanity.com/paper/2406.11085/) (2024)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [A Major Obstacle for NLP Research: Let's Talk about Time Allocation!](https://mlsanity.com/paper/2211.16858/) (2022)

## Built on by (most cited first, 4 of 4)

- [Massively Multilingual Joint Segmentation and Glossing](https://mlsanity.com/paper/2601.10925/) (2026)
- [Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvan](https://mlsanity.com/paper/2603.00923/) (2026)
- [CWoMP: Morpheme Representation Learning for Interlinear Glossing](https://mlsanity.com/paper/2603.18184/) (2026)
- [GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings](https://mlsanity.com/paper/2606.04367/) (2026)

## 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