“Leveraging High-Resource Languages to Improve Low-Resource Language Processing”

ABSTRACT: Recent years have seen dramatic strides in automatic speech and language processing, ranging from automatic speech recognition to machine translation. While these advances have benefited from improvements in machine learning algorithms, they are crucially dependent upon increases in processing power and especially on huge corpora of language data for training and tuning of models. As a result, these language processing systems are accessible only to the few hundred most-resourced languages and remain largely out of reach for the other over six thousand languages of the world, most of which are low-resource or endangered. To bridge this gap, this talk explores approaches to leverage linguistic resources from higher-resource languages to improve effectiveness on language processing tasks. We find that careful integration of within-language resources with selected high-density language resources can enable rapid development and better generalization of both machine translation and spoken language processing capabilities for low-resource languages.

* Co-sponsored by the Boston University Department of Computer Science and the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. We also gratefully acknowledge support from the Office of the Associate Dean for the Humanities in the College of Arts & Sciences.