Fri, Dec 02, 2022 @ 11:00 AM - 02:00 PM
Thomas Lord Department of Computer Science
Ph.D. Candidate: Ehsan Qasemi
Title: Multi-Modal Preconditioned Commonsense Inference
Muhao Chen, Aiichiro Nakano, Daniel O\'Leary, Fred Morstatter, Luis Garcia
Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that \"a glass is used for drinking water\", unless \"the glass is broken\" or \"the water is toxic\". Despite state-of-the-art (SOTA) models\' impressive performance in inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions.
In this dissertation, I initially propose a novel challenge of reasoning with preconditions attributed to commonsense knowledge, design three tasks based on the challenge in text-only setup, and show there is a significant gap between SOTA language models\' performance and human\'s on our tasks. I then use weak supervision in a combination of targeted fine-tuning strategies to improve the language model\'s performance on the preconditioned inference task. Finally, I go beyond the text-only setup and investigate the problem of preconditioned inference in a multi-modal setup when the model is challenged to infer the preconditions from an image.
Zoom link: https://usc.zoom.us/j/92119832136 Date: Friday Dec 2nd, 11AM-12 PM
WebCast Link: Ph.D. Candidate: Ehsan Qasemi Title: Multi-Modal Preconditioned Commonsense Inference Committee: Muhao Chen, Aiichiro Nakano, Daniel O'Leary, Fred Morstatter, Luis Garcia Humans can seamlessly reason with circumstantial preconditions of commonsense knowle
Audiences: Everyone Is Invited
Contact: Lizsl De Leon