February 3 Colloquium: "Probabilistic Commonsense Knowledge in Language"

Talk Abstract

Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. However, commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to model and evaluate such knowledge human-like using probabilistic abstractions and principles.

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. We utilize these approaches in two new commonsense datasets (ProtoQA, Commonsense frame completion). The combination of modeling and evaluation methods based on probabilistic principles sheds light on how commonsense knowledge can be incorporated into artificial intelligence models in the future.


Lorraine (Xiang Li) is an incoming assistant professor in the Department of Computer Science. She will work as a young investigator with the Mosaic team at AI2 before joining Pitt in Fall 2023. Previously, she obtained an M.S. in Computer Science from The University of Chicago while conducting research at TTIC. Her research is at the intersection of natural language processing, commonsense reasoning, knowledge representation, and machine learning. More specifically, her research focuses on designing probabilistic models and evaluation methods for implicit commonsense knowledge in language. She regularly serves on program committees in the NLP field, such as ACL, EMNLP, NAACL, ARR.


Sennott Square Building, Room 5317 


Friday, February 3 at 2:00 p.m. to 3:30 p.m.

News Type