At this week’s MCQLL meeting, Benjamin LeBrun will be presenting Inferring meaning from visually grounded language via symbolic generative programs.

When:
Tuesday, March 21, 15:00–16:00 (Montréal time, UTC-4)
Where:
MCQLL meetings this semester are in hybrid format. We will meet in-person in room 117 of the McGill Linguistics Department, 1085 Dr-Penfield. If you’d like to attend virtually, the Zoom link is here.

All are welcome to attend.

  • Speaker:
    Benjamin LeBrun
    Title:
    Inferring meaning from visually grounded language via symbolic generative programs
    Abstract:

    Humans can infer precise meanings from natural language statements about the physical world in real time, and can learn new lexical concepts from just a single positive example. In contrast, large multi-modal neural models struggle to infer precise meanings for language making reference to the external world. This talk will present ongoing work which integrates insights from cognitive science and probabilistic programming to ground the meaning of natural language expressions in symbolic representations of 3D scenes. I will present prototype models which can infer precise meanings for spatial language where large multi-modal models fail and predict the time-course of grounded online language processing, as well as preliminary work towards a model which can learn meanings for nouns given a single ambiguous example.