Higher-order Comparisons of Sentence Encoder Representations

Mostafa Abdou, Artur Kulmizev, Felix Hill, Daniel M. Low, Anders Søgaard

Abstract
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.
Anthology ID:
D19-1593
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5838–5845
Language:
URL:
https://aclanthology.org/D19-1593
DOI:
10.18653/v1/D19-1593
Bibkey:
Cite (ACL):
Mostafa Abdou, Artur Kulmizev, Felix Hill, Daniel M. Low, and Anders Søgaard. 2019. Higher-order Comparisons of Sentence Encoder Representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5838–5845, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Higher-order Comparisons of Sentence Encoder Representations (Abdou et al., EMNLP-IJCNLP 2019)
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