CS Colloquium
Vivek Srikumar, University of Utah
Refreshments from Koffee will be provided
Host: Rex Ying
Title: Robust NLP: Can We Do Better Than Bigger?
Abstract:
The robust text understanding and generation capabilities of today’s NLP has been driven by the assumption that larger models and larger datasets improve predictive performance. However, this approach may not be sufficient to address real-world challenges, particularly in low-resource languages and domains.
In this talk, I will argue that robust NLP requires us to move beyond accuracy and perplexity as evaluation metrics. Towards this goal, I will present the idea of learning from knowledge rather than data, which exploits input and label meaning, in the presence of invariant domain knowledge. This approach generalizes traditional machine learning and has shown promise across multiple NLP problems. I will connect back to the theme of low-resource domains by presenting a text-based crisis counseling application. I will conclude by outlining future research directions around the theme of making inferences about text across domains despite limited data and compute resources.
Bio:
Vivek Srikumar is an associate professor in the Kahlert School of Computing at the University of Utah. His research lies in the areas of artificial intelligence, natural language processing and machine learning, and has been primarily driven by questions arising from the need to efficiently reason about textual data with limited supervision. His research has been published at various AI, NLP and ML venues, and has been recognized by a paper award at EMNLP 2014, and honorable mentions from CoNLL 2019 and the IEEE Micro magazine. His work has been supported by research grants from NSF, US-Israel BSF, NIH, and awards from Google, Intel, Nvidia and Verisk. He has served as associate program chair of AAAI 2022 and the program co-chair of CoNLL 2022 and ACL 2024. Furthermore, he has organized several workshops hosted at the primary ML and NLP conferences around the theme of how learning and structured knowledge intersect. He was a post-doctoral scholar at Stanford University before moving to Utah, and prior to that, in 2013, he obtained his PhD from the University of Illinois at Urbana-Champaign.
Website: https://svivek.com