Multilingual Statistical Machine Translation

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This proposal is about machine translation from one natural language to another, such as translation from English to French. As a bilingual and multicultural nation, translation into multiple languages is socially important to Canada. Translation is also strategically important to Canada in order to facilitate our interactions with the world. There have been tremendous advances in the field of machine translation over the last two decades with rule-based approaches being replaced by statistical machine translation. Statistical machine translation solutions are now being offered by major companies like Google, SDL and IBM. However all contemporary translation systems rely on large amounts of data between specific language pairs such as French-English, ignoring many low-resource languages, e.g. Inuktitut, Haitian Kreyol, Malayalam, and Finnish (languages that are spoken by millions of people). This proposal aims to use linguistic insights about language families and use this information to develop multilingual statistical machine translation models and algorithms. We aim to transfer information from source-target language pairs with a lot of resources, such as French-English to low-resource language pairs, such as English-Inuktitut. This proposal addresses a basic unresolved issue in statistical machine translation research: learning a translation model between any pair of languages regardless of resource limitations.