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.

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

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Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

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

NLP Highlights