@inproceedings{abdou-etal-2019-higher,
title = "Higher-order Comparisons of Sentence Encoder Representations",
author = "Abdou, Mostafa and
Kulmizev, Artur and
Hill, Felix and
Low, Daniel M. and
S{\o}gaard, Anders",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1593",
doi = "10.18653/v1/D19-1593",
pages = "5838--5845",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Higher-order Comparisons of Sentence Encoder Representations
%A Abdou, Mostafa
%A Kulmizev, Artur
%A Hill, Felix
%A Low, Daniel M.
%A Søgaard, Anders
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F abdou-etal-2019-higher
%X 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.
%R 10.18653/v1/D19-1593
%U https://aclanthology.org/D19-1593
%U https://doi.org/10.18653/v1/D19-1593
%P 5838-5845
Markdown (Informal)
[Higher-order Comparisons of Sentence Encoder Representations](https://aclanthology.org/D19-1593) (Abdou et al., EMNLP-IJCNLP 2019)
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.