Shah K, Barrault L & Schwenk H (2012) A general framework toweight heterogeneous parallel data for model adaptation in statistical machine translation.He is one of the organisers of the Multimodal Machine Translation shared task at WMT. LoГЇc's research work focuses on statistical and neural machine translation, by including linguistics aspects (factored neural machine translation), by considering multiple modalities (multimodal neural machine translation) and by designing lifelong learning methods for MT. He coordinated the EU ChistERA M2CR project and is currently actively involved in the ChistERA ALLIES project. LoГЇc Barrault participated in many international projects, namely EuroMatrix+, MateCAT, DARPA BOLT, and national projects, namely ANR Cosmat, вЂњProjet dвЂ™Investissement dвЂ™AvenirвЂќ PACTE and a large industrial project PEA TRAD. He then did 2 years as researcher and 9 years as Associate Professor at LIUM, Le Mans UniversitГ© working on statistical and neural machine translation. He obtained his PhD at the University of Avignon in 2008 in the field of automatic speech recognition. LoГЇc Barrault (M) was a Senior Lecturer in the Natural Language Processing group at the University of Sheffield until August 2021 and is now a visiting academic in the Department of Computer Science. Orcid ID:0000-0002-0634-6147 Google Scholar Personal website Profile Conferences, events, visitor accommodation and weddings.Worldwide Universities Network at Sheffield.Research centres, institutes and networks.Coronavirus: our research and innovation.Information for current staff and students.

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