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Inserting Information Bottlenecks for Attribution in Transformers

2020-12-27 00:35:43
Zhiying Jiang, Raphael Tang, Ji Xin, Jimmy Lin

Abstract

Pretrained transformers achieve the state of the art across tasks in natural language processing, motivating researchers to investigate their inner mechanisms. One common direction is to understand what features are important for prediction. In this paper, we apply information bottlenecks to analyze the attribution of each feature for prediction on a black-box model. We use BERT as the example and evaluate our approach both quantitatively and qualitatively. We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers. We demonstrate that our technique outperforms two competitive methods in degradation tests on four datasets. Code is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13838

PDF

https://arxiv.org/pdf/2012.13838.pdf


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