Explore the words cloud of the DELPHI project. It provides you a very rough idea of what is the project "DELPHI" about.
The following table provides information about the project.
TEL AVIV UNIVERSITY
|Coordinator Country||Israel [IL]|
|Total cost||1˙499˙375 €|
|EC max contribution||1˙499˙375 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2019-04-01 to 2024-03-31|
Take a look of project's partnership.
|1||TEL AVIV UNIVERSITY||IL (TEL AVIV)||coordinator||1˙499˙375.00|
The explosion of information around us has democratized knowledge and transformed its availability for people around the world. Still, since information is mediated through automated systems, access is bounded by their ability to understand language. Consider an economist asking “What fraction of the top-5 growing countries last year raised their co2 emission?”. While the required information is available, answering such complex questions automatically is not possible. Current question answering systems can answer simple questions in broad domains, or complex questions in narrow domains. However, broad and complex questions are beyond the reach of state-of-the-art. This is because systems are unable to decompose questions into their parts, and find the relevant information in multiple sources. Further, as answering such questions is hard for people, collecting large datasets to train such models is prohibitive. In this proposal I ask: Can computers answer broad and complex questions that require reasoning over multiple modalities? I argue that by synthesizing the advantages of symbolic and distributed representations the answer will be “yes”. My thesis is that symbolic representations are suitable for meaning composition, as they provide interpretability, coverage, and modularity. Complementarily, distributed representations (learned by neural nets) excel at capturing the fuzziness of language. I propose a framework where complex questions are symbolically decomposed into sub-questions, each is answered with a neural network, and the final answer is computed from all gathered information. This research tackles foundational questions in language understanding. What is the right representation for reasoning in language? Can models learn to perform complex actions in the face of paucity of data? Moreover, my research, if successful, will transform how we interact with machines, and define a role for them as research assistants in science, education, and our daily life.
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The information about "DELPHI" are provided by the European Opendata Portal: CORDIS opendata.