2 research outputs found
Higher-order Comparisons of Sentence Encoder Representations
Representational Similarity Analysis (RSA) is a technique developed by
neuroscientists for comparing activity patterns of different measurement
modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has
several advantages over existing approaches to interpretation of language
encoders based on probing or diagnostic classification: namely, it does not
require large training samples, is not prone to overfitting, and it enables a
more transparent comparison between the representational geometries of
different models and modalities. We demonstrate the utility of RSA by
establishing a previously unknown correspondence between widely-employed
pretrained language encoders and human processing difficulty via eye-tracking
data, showcasing its potential in the interpretability toolbox for neural
modelsComment: EMNLP 201