28th October 2021 - 4pm - Jacob Eisenstein: Seminar

Title: Trustworthy NLP: A Causal Perspective

Abstract:

Natural language processing systems have achieved high levels of accuracy on many benchmark datasets, yet it is unclear whether these same systems can be trusted in high-stakes settings. This talk identifies some key desiderata for more trustworthy natural language processing, and argues that progress can be made by treating learning and inference as causal systems. In particular, I will focus on counterfactual invariance – the notion that predictions should be invariant to interventions on causally-irrelevant variables, which can be formalized in checklist-style evaluations. I will present a method for achieving counterfactual invariance by designing regularizers around the causal structure of the data-generating process. For some causal structures, the resulting counterfactually-invariant predictors are optimal with respect to a class of "causally-compatible" perturbations of the data generating process. Empirically, these predictors avoid spurious correlations and transfer well to new data distributions. I will also touch on other connections between causality and natural language processing highlighted in the recent survey by Feder et al (2021). 

Bio:

Jacob Eisenstein is a research scientist at Google, where he is focused on making language technology more robust and trustworthy. He was previously on the faculty of the Georgia Institute of Technology, where he supervised six successful doctoral dissertations, received the NSF CAREER Award for research on computational sociolinguistics, and wrote a textbook on natural language processing. He completed his Ph.D. at MIT with a dissertation on computational models of speech and gesture.

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