News

Through an extensive evaluation, this work offers insights into underlying causes of biases, presenting targeted mitigation strategies tailored to alleviate gender and racial biases, thereby engendering automatic segmentation results that are fair, impartial, and safe in the context of AI. 

This talk will introduce a probabilistic model representing commonsense knowledge using a learned latent space of geometric embeddings -- probabilistic box embeddings. Using box embeddings makes it possible to handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, we show limitations with current Large Language models with their (commonsense) reasoning ability. 

I present three of our recent works: 1) Discourse-aware generation models for automatic social media moderation and mediation, 2) Sign language processing, and 3) Equitable and human-like dialogue generation models based on learning theory. Finally, I describe my research vision: Building inclusive and collaborative communicative systems and grounded artificial intelligence models by leveraging the cognitive science of language use alongside formal methods of machine learning.

This talk will introduce a probabilistic model representing commonsense knowledge using a learned latent space of geometric embeddings -- probabilistic box embeddings. Using box embeddings makes it possible to handle commonsense queries with intersections, unions, and negations in a way similar to Venn diagram reasoning. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. To fill in the gap, I will discuss a method of retrieving commonsense related question answer distributions from human annotators and a novel method of generative evaluation.