Objective This project will develop a unifying framework of novel methods for sequence classification and thus make a major break-through in automatic speech recognition and machine translation, advancing these areas of human language technology (HLT) beyond state-of-the-art. Despite the huge progress made in the field, the specific aspect of sequence classification has not been addressed adequately in the past research in these disciplines and remains a big challenge. The proposed project will provide a novel framework under consistent consideration of the leading aspect of sequence classification. It will break the ground for a deeper, more comprehensive foundation for sequence classification and pave the way for a new generation of algorithms that will put human language technology on a more solid basis and that will accelerate progress in the field across several disciplines. The leading research objectives are: 1. A novel theoretical framework for sequence classification. 2. Consistent sequence modeling across training and testing, which is specifically lacking in machine translation. 3. Adequate sequence-level performance-aware training criteria to learn the free parameters of the models. 4. Investigation of (true) unsupervised training for HLT sequence classification: its principles, its prerequisites, its limitations and its practical usage. The study of these four problems will provide key enabling techniques for HLT sequence classification in general that will carry over to and create high impact on the areas of speech recognition, machine translation and handwritten text recognition. Using our top-ranking research prototype systems, we will verify the validity and effectiveness or our research on public international benchmarks. Fields of science humanitieslanguages and literaturegeneral language studiesnatural sciencescomputer and information sciencesdatabasessocial scienceseconomics and businessbusiness and managementcommercee-commercenatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-ADG-2015 - ERC Advanced Grant Call for proposal ERC-2015-AdG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Host institution RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Net EU contribution € 2 500 000,00 Address TEMPLERGRABEN 55 52062 Aachen Germany See on map Region Nordrhein-Westfalen Köln Städteregion Aachen Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 500 000,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN Germany Net EU contribution € 2 500 000,00 Address TEMPLERGRABEN 55 52062 Aachen See on map Region Nordrhein-Westfalen Köln Städteregion Aachen Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 500 000,00