Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations
Xingyuan Zhao, Satoru Ozaki, Antonios Anastasopoulos, Graham Neubig, Lori Levin
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 glossing 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 mechanism, 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.
BibTeX
@inproceedings{zhao-etal-2020-automatic,
title = "Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations",
author = "Zhao, Xingyuan and
Ozaki, Satoru and
Anastasopoulos, Antonios and
Neubig, Graham and
Levin, Lori",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.471/",
doi = "10.18653/v1/2020.coling-main.471",
pages = "5397--5408"
}