Dialogue Modelling for Statistical Machine Translation 1 Relevance relative to the call for proposals

The proposed project sets out to improve the quality of machine translation (MT) technology. Machine translation, known by the general public through popular applications such as Google Translate, is defined as the automatic translation from one language to another through a computer algorithm – for instance, translating a text from Japanese to Norwegian or vice-versa. In a globalised world where the ability to access and produce multilingual content is becoming ever more important, machine translation has become a R&D area of strategic significance for both economic and cultural reasons. In addition to helping millions of people meet their daily translation needs, machine translation can also facilitate the dissemination of information across multiple linguistic communities, and thereby help preserve smaller languages and promote cultural diversity. The project outlined in this proposal concentrates on a particular type of translation task, namely the automatic translation of (live or transcribed) dialogues. This type of translation comprises several important application domains such as the translation of subtitles for audiovisual content, the circulation of meeting transcripts across multiple languages, and (most difficult of all) speech-to-speech interpretation between individuals speaking different languages. The project’s key idea is to improve the translation quality in these domains by incorporating contextual dialogue knowledge into the translation pipeline. The project will develop new models, tools and algorithms that allow various contextual factors to be automatically extracted from the dialogue history and integrated into the translation models of a statistical machine translation system. The resulting “dialogue-aware” models will therefore be able to dynamically adapt their translation outputs according to the current dialogue context. The project aims to demonstrate that such contextrich approach is able to produce translations of a higher quality than standard methods. To this end, the project will include both theoretical investigations related to the formalisation and estimation of these rich statistical models and experimental work to implement these ideas in a full-fledged machine translation architecture. To demonstrate the practical significance of the project, the translation models and software tools developed through the project will be evaluated in a concrete application domain of high industrial relevance: the automatic translation of subtitles. The project stands at the interface between two research areas: dialogue modelling on the one hand, and statistical machine translation on the other. These two fields have so far developed in relative isolation. The proposed project aspires to create a bridge between them and show how dialogue modelling methods can be fruitfully leveraged in statistical machine translation. To our knowledge, such an approach has never been thoroughly investigated before. The research realised through the course of the project is therefore expected to contribute to substantial scientific and technological advances in both dialogue modelling and machine translation. To achieve this ambitious goal, the project will feature strong international collaborations with leading researchers in the field of machine translation. In particular, long research stays are planned both to the Swiss Federal Institute of Technology and the Department of Linguistics at Uppsala University to conduct joint research work on some of the key scientific challenges described in this proposal. In addition, the project is also proud to include two well-known industrial partners, NRK and Broadcast Text International, who will contribute to the project by providing us with a privileged access to their archives of professionally translated subtitles. These archives will be exploited to construct high-quality translation models for the project’s application domain. Owing to the innovative character of the envisioned research, its focus on foundational aspects of natural language processing technology and its clear international outlook, the FRINATEK program provides an excellent fit for the proposed work.