130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn

In this episode, we talk with Lisa Beinborn, an assistant professor
at Vrije Universiteit Amsterdam, about how to use human cognitive
signals to improve and analyze NLP models. We start by discussing
different kinds of cognitive signals—eye-tracking, EEG, MEG, and
fMRI—and challenges associated with using them. We then turn to
Lisa’s recent work connecting interpretability measures with
eye-tracking data, which reflect the relative importance measures
of different tokens in human reading comprehension. We discuss
empirical results suggesting that eye-tracking signals correlate
strongly with gradient-based saliency measures, but not attention,
in NLP methods. We conclude with discussion of the implications of
these findings, as well as avenues for future work. Papers
discussed in this episode: Towards best practices for leveraging
human language processing signals for natural language processing:
https://api.semanticscholar.org/CorpusID:219309655 Relative
Importance in Sentence Processing:
https://api.semanticscholar.org/CorpusID:235358922 Lisa Beinborn’s
webpage: https://beinborn.eu/ The hosts for this episode are Alexis
Ross and Pradeep Dasigi.