Fall 2022 Schedule

Date	Title
9.2.20	Planning
9.9.20
9.16.20	Title: Fads, Fallacies and Fantasies in the Name of Machine Learning - Vivek Srikumar Abstract: The pervasiveness of machine learning, and artificial intelligence powered by it, is clear from even a cursory overview of the last several years of academic literature and mainstream technology reporting. The goal of this talk is to provoke thought and discussions about the future of the field. To this end, I will talk about how, as a field, applied machine learning may be starting to bind itself into an intellectual monoculture. In particular, I will describe specific blinders that we may find hard to shake off: (a) the obsession with ranking and leaderboarding, (b) the assumption that purely data-driven computing is always the right answer, and (c) the excessive focus on clean toy problems in lieu of working with real data. Along the way, we will see several examples of questions that we may be able to think about if we cast aside these blinders. Bio: Vivek Srikumar is associate professor in the School of Computing at the University of Utah. His research lies in the areas of natural learning processing and machine learning and has primarily been driven by questions arising from the need to reason about textual data with limited explicit supervision and to scale NLP to large problems. His work has been published in various AI, NLP and machine learning venues and has been recognized by paper awards from EMNLP and CoNLL. His work has been supported by awards from NSF, BSF and NIH, and also from several companies.. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2013 and was a post-doctoral scholar at Stanford University.
9.23.20
9.30.20	Jon's Prelim
10.7.20	Peter Foltz "NLP for Team Communication Analysis"
10.14.20	Tao Li (Utah CS PhD student) - Structured Tuning for Semantic Role Labeling, ACL 2020 Authors: Tao Li, Parth Anand Jawale, Martha Palmer, Vivek Srikumar, https://www.aclweb.org/anthology/2020.acl-main.744.pdf Abstract: Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.
10.21.20
10.28.20	Chelsea Proposal
11.4.20	Skatje Proposal?
11.11.20	Rehan Proposal
11.18.20	NAACL submission workshop
11.25.20	Fall Break
12.2.20	Tentative Abhidip's proposal
12.9.20	Tentative Vivian proposal

Past Schedules

• Spring 2020 Schedule
• Fall 2019 Schedule
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