My research focuses on Natural Language Processing and Machine Learning.
I study computational models of natural language meaning - especially sentence-level meaning representations and natural language inference.
I conduct research on representation learning of natural language meaning in a multilingual setting, utilizing multilingual sentence representations in various transfer learning tasks.
I develop machine translation models and state-of-the-art neural machine translation systems.
I'm involved in two large-scale research projects.
Found in Translation: Natural Language Understanding with Cross-lingual Grounding is an ERC funded project led by Jörg Tiedemann. With this project, we propose a line of research that will focus on the development of novel data-driven models that can learn language-independent abstract meaning representations from indirect supervision provided by human translations covering a substantial proportion of the linguistic diversity in the world. A guiding principle is cross-lingual grounding, the effect of resolving ambiguities through translation. Eventually, this will lead to language-independent meaning representations and we will test our ideas with multilingual machine translation and tasks that require semantic reasoning and inference.
MeMAD project provides novel methods for efficient re-use and re-purpose of multilingual audiovisual content. These methodologies revolutionize video management and digital storytelling in broadcasting and media production. We go far beyond the state-of-the-art automatic video description methods by making the machine learn from the human. The resulting description is thus not only a time-aligned semantic extraction of objects but makes use of the audio and recognizes action sequences. In the MeMad project my role is to study multimodal and document-level machine translation.