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Neural Machine Translation for Bilingually Low-Resource Scenarios

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thesis
posted on 30.01.2020, 22:38 by POORYA ZAREMOODI
Artificial intelligence is revolutionising the language translation industry by efficiently bridging the barriers of language in different areas. This approach, called Machine Translation (MT), is a data-hungry technology. However, we do not have the luxury of having large amounts of labelled training data for many languages. This thesis aims to improve the quality of MT for low-resource languages by leveraging linguistic resources.

History

Campus location

Australia

Principal supervisor

Gholamreza Haffari

Additional supervisor 1

Wray Buntine

Additional supervisor 2

Sarvnaz Karimi

Year of Award

2020

Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

CSIRO Data61

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports