Computer Science > Computation and Language
[Submitted on 1 Sep 2019 (v1), last revised 5 Sep 2019 (this version, v2)]
Title:Higher-order Comparisons of Sentence Encoder Representations
View PDFAbstract: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 models
Submission history
From: Mostafa Abdou [view email][v1] Sun, 1 Sep 2019 02:13:12 UTC (4,277 KB)
[v2] Thu, 5 Sep 2019 11:12:56 UTC (4,277 KB)
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