Project description
Training speech recognition algorithms to speak more languages
Say hello to Apple’s Siri, Amazon’s Echo and Google’s Assistant. But in which language? These task-based statistical dialogue systems (SDSs) are not available in all languages. This limits the global reach of conversational artificial intelligence (AI). The EU-funded MultiConvAI project will develop the first prototype system for scaling conversational AI to multiple languages. Based on new methodology that learns multilingual word representations, this new system will use a process called semantic specialisation. The project will develop Natural Language Understanding (NLU) modules for SDSs via more effective semantic specialisation based on joint multi-source, multi-target training. It will also focus on typologically diverse languages.
Objective
In recent past, Conversational Artificial Intelligence (AI) has made major advances, thanks to the availability of big data and increasingly powerful deep learning. Task-based statistical dialogue systems (SDS) are now viable, embedded in popular commercial applications (e.g. the Apple’s Siri, Amazon’s Echo, Google’s Assistant) and cost-effective in many scenarios (e.g. customer support, call centre service, searching, booking). Yet current SDSs are only available for a handful of resource-rich languages, leaving the majority of the worlds languages and their speakers behind. Our project will develop the first prototype system for scaling conversational AI to multiple languages. This will be based on new methodology that learns multilingual word representations (i.e. embeddings, WEs) without the need for expensive training data, using a process called semantic specialisation that complements WEs with common-sense and linguistic knowledge in external knowledge graphs. Building on our promising pilot studies, we will develop Natural Language Understanding (NLU) modules for SDS via 1) more effective semantic specialisation based on joint multi-source multi-target training; and 2) focus on typologicallydiverse languages. We foresee a pioneering use of selective sharing and structural adaptation for obtaining WEs and optimisation for the target languages guided by typological knowledge. The best resulting technology will be integrated in a demo prototype system which users and industries can deploy to generate multilingual NLU input for more widely portable SDS. Since we also plan to explore the possibility to form a start-up company, we will use the system to demonstrate the potential to our network of industry contacts and potential customers. On a larger scale, extending the multilingual scope of SDSs can have major socioeconomic benefits: it can broaden the global reach of conversational AI and it can enhance its commercial viability.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences knowledge engineering
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-POC-LS - ERC Proof of Concept Lump Sum Pilot
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2020-PoC
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
CB2 1TN CAMBRIDGE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.