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Document-wide Neural Machine Translation

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thesis
posted on 12.11.2019, 23:52 by SAMEEN MARUF
Machine translation (MT) is an important task in natural language processing as it automates the translation process and reduces the reliance on human translators. The goal of MT is to generate translations of a given text in a source language to that in the target language. With the advent of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques. However, most of the neural translation models still perform a sentence-by-sentence translation, ignoring all extra-sentential information. This research aims to build efficient neural models for document-level translation, which incorporate global contextual information when translating sentences. Our experimental results confirm the significance of leveraging document-wide context information for improving translation quality.

History

Campus location

Australia

Principal supervisor

Gholamreza Haffari

Additional supervisor 1

Geoffrey I. Webb

Additional supervisor 2

André F. T. Martins

Year of Award

2019

Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

Clayton School of Information Technology

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports