Document-wide Neural Machine Translation

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.