Monash University
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Document-wide Neural Machine Translation

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posted on 2019-11-12, 23:52 authored 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.


Campus location


Principal supervisor

Gholamreza Haffari

Additional supervisor 1

Geoffrey I. Webb

Additional supervisor 2

André F. T. Martins

Year of Award


Department, School or Centre

Information Technology (Monash University Clayton)


Doctor of Philosophy

Degree Type



Faculty of Information Technology