# Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages

arXiv: https://arxiv.org/abs/2203.09632 · HTML: https://mlsanity.com/paper/2203.09632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 57 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://mlsanity.com/paper/2106.02124/) (2021)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [SD-QA: Spoken Dialectal Question Answering for the Real World](https://mlsanity.com/paper/2109.12072/) (2021)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [Challenges of language technologies for the indigenous languages of the Americas](https://mlsanity.com/paper/1806.04291/) (2018)
- [Pushing the Limits of Low-Resource Morphological Inflection](https://mlsanity.com/paper/1908.05838/) (2019)
- [Learning to Recognize Dialect Features](https://mlsanity.com/paper/2010.12707/) (2020)
- [Massively Multilingual Adversarial Speech Recognition](https://mlsanity.com/paper/1904.02210/) (2019)
- [Utility is in the Eye of the User: A Critique of NLP Leaderboards](https://mlsanity.com/paper/2009.13888/) (2020)
- [Machine Translation into Low-resource Language Varieties](https://mlsanity.com/paper/2106.06797/) (2021)
- [Bootstrapping Techniques for Polysynthetic Morphological Analysis](https://mlsanity.com/paper/2005.00956/) (2020)

## Built on by (most cited first, 2 of 2)

- ["It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies](https://mlsanity.com/paper/2402.02639/) (2024)
- [Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation](https://mlsanity.com/paper/2509.10644/) (2025)

## Abstract (arXiv)

Recent progress in NLP is driven by pretrained models leveraging massive datasets and has predominantly benefited the world's political and economic superpowers. Technologically underserved languages are left behind because they lack such resources. Hundreds of underserved languages, nevertheless, have available data sources in the form of interlinear glossed text (IGT) from language documentation efforts. IGT remains underutilized in NLP work, perhaps because its annotations are only semi-structured and often language-specific. With this paper, we make the case that IGT data can be leveraged successfully provided that target language expertise is available. We specifically advocate for collaboration with documentary linguists. Our paper provides a roadmap for successful projects utilizing IGT data: (1) It is essential to define which NLP tasks can be accomplished with the given IGT data and how these will benefit the speech community. (2) Great care and target language expertise is required when converting the data into structured formats commonly employed in NLP. (3) Task-specific and user-specific evaluation can help to ascertain that the tools which are created benefit the target language speech community. We illustrate each step through a case study on developing a morphological reinflection system for the Tsimchianic language Gitksan.