Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals

Author:

Elazar Yanai12,Ravfogel Shauli13,Jacovi Alon4,Goldberg Yoav15

Affiliation:

1. Computer Science Department, Bar Ilan University

2. Allen Institute for Artificial Intelligence. yanaiela@gmail.com

3. Allen Institute for Artificial Intelligence. shauli.ravfogel@gmail.com

4. Computer Science Department, Bar Ilan University. alonjacovi@gmail.com

5. Allen Institute for Artificial Intelligence. yoav.goldberg@gmail.com

Abstract

Abstract A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded. Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation. Equipped with this new analysis tool, we can ask questions that were not possible before, for example, is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.1

Publisher

MIT Press - Journals

Reference43 articles.

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4. What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties;Conneau,2018

5. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin,2019

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