Non-traditional resources and improved tools for low-resource machine translation

Monday, February 12, 2018, 3:00 pm - 4:00 pm PDTiCal
Conf. Rm #1135
This event is open to the public.
NL Seminar
Nima Pourdamghani (USC/ISI)

Abstract: Thanks to massive training data, and powerful machine translation techniques, machine translation quality has reached acceptable levels for a handful of languages. However, for hundreds of other languages, translation quality decreases quickly as the size of the available training data becomes smaller. For languages with a few millions or less tokens of translation data (called low-resource languages in this dissertation) traditional machine translation technologies fail to produce understandable translations into English. In this work, I explore various non-traditional sources for improving low-resource machine translation.

Bio: Nima Pourdamghani is a phd student at USC/ISI working with professor Kevin Knight. Nima's interests are natural language processing, and applications of machine learning in general. His phd thesis is on building tools to improve machine translation for hundreds of low-resource languages.

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