Neural Machine Translation for Bilingually Low-Resource Scenarios

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.