Multimodal Neural Machine Translation
PhD Student: Ozan Caglayan
Advisor(s): Paul Deléglise (LIUM, LST)
Co-advisor(s): Loïc Barrault (LIUM, LST) & Fethi Bougares (LIUM, LST)
Funding: M2CR Project
This thesis aims at developing neural machine translation architectures by integrating different types of informations to improve the quality of the generated translations. More specifically, we are interested to guide the neural network towards a multimodal representational space by making use of parallel and multimodal corpora with images and their descriptions in multiple languages.