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Recent progress in NLP is driven by pretrained models leveraging massivedatasets and has predominantly benefited the world's political and economicsuperpowers. Technologically underserved languages are left behind because theylack such resources. Hundreds of underserved languages, nevertheless, haveavailable data sources in the form of interlinear glossed text (IGT) fromlanguage documentation efforts. IGT remains underutilized in NLP work, perhapsbecause its annotations are only semi-structured and often language-specific.With this paper, we make the case that IGT data can be leveraged successfullyprovided that target language expertise is available. We specifically advocatefor collaboration with documentary linguists. Our paper provides a roadmap forsuccessful projects utilizing IGT data: (1) It is essential to define which NLPtasks can be accomplished with the given IGT data and how these will benefitthe speech community. (2) Great care and target language expertise is requiredwhen converting the data into structured formats commonly employed in NLP. (3)Task-specific and user-specific evaluation can help to ascertain that the toolswhich are created benefit the target language speech community. We illustrateeach step through a case study on developing a morphological reinflectionsystem for the Tsimchianic language Gitksan.

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