Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Despite tools such as Siri and Alexa – which may suggest that the human-like systems in Her and Ex Machina are just around the corner – realistic natural language interaction with a computer remains an elusive goal. Recent “big data” approaches can achieve impressive behaviours in text and speech processing, but human-like processing of language remains one of the grand challenges of artificial intelligence (AI). Capturing the complexity and extensibility of word meanings is a major obstacle to achieving that goal, yet humans learn word meanings and adapt them to new situations almost effortlessly. Only by modeling language abilities as a computational cognitive system can we hope to achieve human-like performance in future AI applications. The specific objective of this proposal is to devise state-of-the-art computational representations and algorithms that reflect leading-edge linguistic and psychological theories, with the goal of achieving human-like behaviour in representing, learning, and interpreting the meaning of words, leading to three related sets of research projects.

The first set of projects will contribute novel algorithms drawing on alignment and graph methods from natural language processing (NLP) that can efficiently yield crosslinguistic semantic data structures. We will demonstrate that these representations more accurately capture properties of human semantic knowledge, and can thus support improved automatic language analysis tools. The second set of projects will demonstrate the benefit of adapting state-of-the-art machine learning techniques to integrate cognitive influences on word learning. Such techniques will enable us to extend the scientific understanding of word learning to acquisition of structured and abstract meanings. Given their basis in leading-edge computer science approaches, these findings can inform NLP methods for extracting rich semantic relations and exploiting top-down and bottom-up information in acquiring meaning. Our third set of projects will extend the state-of-the-art in cognitive models of reference by integrating incremental probabilistic learning methods that reflect cognitive factors. By developing models that capture experimentally-demonstrated influences and that adapt over the course of a conversation, we will contribute to increased understanding of the factors that must be reflected in NLP systems to match human expectations in conversation. Overall, the projects here demonstrate the dual benefits of the multidisciplinary research program: Bringing advances in computer science to bear on improving our understanding of human cognition as a computational system, and informing NLP by adapting recent linguistic and psycholinguistic insights within a computational framework.