Categoria:
Seminário
Onde:
Híbrido
Local:
Sala de Seminários do DI e ZOOM
Descrição:
This talk delves into the intricacies of uncertainty in human-machine dialogue, mainly focusing on the challenges and solutions related to ambiguities arising from impoverished contextual representations. We examine how linguistically informed context representations can mitigate data-related uncertainty in a deployed dialogue system similar to Alexa. We acknowledge that certain types of data-related uncertainty are unavoidable and investigate the capabilities of modern billion-scale language models in representing this form of uncertainty in conversations. Shifting our focus to epistemic uncertainty arising from misaligned background knowledge between humans and machines, we explore strategies for quantifying and reducing this form of uncertainty.
