Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations

Proceedings of the 28th International Conference on Computational Linguistics, pages 5397–5408 Barcelona, Spain (Online), December 8-13, 2020 5397 Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations Xingyuan Zhao†, Satoru Ozaki†, Antonios Anastasopoulos‡, Graham Neubig†, Lori Levin† †Language Technologies Institute, Carnegie Mellon University ‡Department of Computer Science, George Mason University {xingyuanz,sozaki}@andrew.cmu.edu antonis@gmu.edu {gneubig,lsl}@cs.cmu.edu Abstract Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers. Manual production of IGT takes time and requires linguistic expertise. We attempt to address this issue by creating automatic gloss- ing models, using modern multi-source neural models that additionally leverage easy-to-collect translations. We further explore cross-lingual transfer and a simple output length control mech- anism, further refining our models. Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall. 1 Introduction The under-documentation of endangered languages is an imminent problem for the linguistic commu- nity. Of the 7,000 languages in the world, an estimated 50% will face extinction in the coming decades, while around 35–42% still remain substantially undocumented (Austin and Sallabank, 2011; Seifart et al., 2018). Perhaps these numbers are no surprise when one acknowledges the difficulty of language documentation – fieldwork demands time, cultural understanding, linguistic expertise, and financial sup- port among a myriad of other factors. Consequently, an important task for the linguistic community is to facilitate the otherwise daunting documentation process as much as possible. Documentation is not a cure-all for language loss, but it is an important part of language preservation; even in an unfortunate worst-case scenario where a language does disappear, a permanent record of the language is saved for posterity, and could hopefully be useful for revitalization purposes. Documentation written for a Language A in a Language B necessarily involves careful selection of example data from Language A that illustrate various aspects of Language A’s grammar. These data are often provided in the form of a typical semi-structured format known as interlinear glossed text (IGT). IGT consists of three lines: the first line being the original data from Language A, the third line its trans- lation into Language B, and the line in between the other lines being a morpheme-by-morpheme gloss, i.e. annotation of the Language A data. The provision of this second line illustrates the morphological structure of Language A to the reader, who is not necessarily informed on the grammar of Language A. An example is outlined in Figure 1. IGT helps linguists better understand how other languages work without prior knowledge of those languages or their grammars. However, manual segmentation and IGT annotation is still a time- and labor-consuming work, as it re- quires linguistic training. Therefore, language documentation projects typically manifest a yawning gap between the amount of material recorded and archived and the amount of data that is thoroughly analyzed with morphological segmentation and gloss (Seifart et al., 2018). This gap can be filled using automatic approaches, which could …

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