Edith Coates


An Inflectional Database for Gitksan
Bruce Oliver | Clarissa Forbes | Changbing Yang | Farhan Samir | Edith Coates | Garrett Nicolai | Miikka Silfverberg
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.

Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Under-Documented Languages
Clarissa Forbes | Farhan Samir | Bruce Oliver | Changbing Yang | Edith Coates | Garrett Nicolai | Miikka Silfverberg
Findings of the Association for Computational Linguistics: ACL 2022

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.


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Expanding the JHU Bible Corpus for Machine Translation of the Indigenous Languages of North America
Garrett Nicolai | Edith Coates | Ming Zhang | Miikka Silfverberg
Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)