Xiang Ren

Xiang Ren
University of Southern California - USC
Commonsense Reasoning in the Wild

Current NLP systems impress us by achieving close-to-human performance on benchmarks of answering commonsense questions or writing interesting stories. However, most of the progress is evaluated using static, closed-ended datasets created for individual tasks. To deploy commonsense reasoning services in the wild, we look to develop and evaluate systems that can generate answers in an open-ended way, perform robust logical reasoning, and generalize across diverse task formats, domains, and datasets. In this talk I will share our effort on introducing new formulations of commonsense reasoning challenges and novel evaluation protocols, towards broadening the scope in approaching machine common sense. We hope that such a shift of evaluation paradigm would encourage more research on externalizing the model reasoning process and improving model robustness and cross-task generalization.


Xiang Ren is an assistant professor and Viterbi Early Career Chair at the USC Computer Science Department, a Research Team Leader at USC ISI, and the director of the Intelligence and Knowledge Discovery (INK) Lab at USC. Priorly, he spent time as a research scholar at Stanford University and received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. Ren's research seeks to build generalizable natural language processing (NLP) systems which can handle a wide variety of language tasks and situations. He works on new algorithms and datasets to make NLP systems cheaper to develop and maintain, arm machine models with common sense, and improve models’ transparency and reliability to build user trust. His research work has received several best paper awards in top NLP and AI conference venues. Ren has been awarded a NSF CAREER Award, multiple faculty research awards from Google, Facebook, Amazon, JP Morgan and Sony, and the 2018 ACM SIGKDD Doctoral Dissertation Award. He was named Forbes' Asia 30 Under 30 in 2019.

Thursday, October 6, 2022 - 17:00