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.
Original language | English |
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Pages (from-to) | 160-175 |
Number of pages | 16 |
Journal | Transactions of the Association for Computational Linguistics |
Volume | 9 |
DOIs | |
State | Published - 1 Feb 2021 |
Bibliographical note
Publisher Copyright:© 2021, MIT Press Journals. All rights reserved.
Funding
This project has received funding from the Europoean Research Council (ERC) under the Europoean Union’s Horizon 2020 research and innovation programme, grant agreement no. 802774 (iEXTRACT). Yanai Elazar is grateful to be partially supported by the PBC fellowship for outstanding PhD candidates in Data Science. We would like to thank Hila Gonen, Amit Moryossef, Divyansh Kaushik, Abhilasha Ravichander, Uri Shalit, Felix Kreuk, Jurica ?eva, and Yonatan Belinkov for their helpful comments and discussions. We also thank the anonymous reviewers and the action editor, Radu Florian, for their valuable suggestions. This project has received funding from the Europoean Research Council (ERC) under the Europoean Union?s Horizon 2020 research and innovation programme, grant agreement no. 802774 (iEXTRACT). Yanai Elazar is grateful to be partially supported by the PBC fellowship for outstanding PhD candidates in Data Science.
Funders | Funder number |
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Europoean Union?s Horizon 2020 research and innovation programme | |
Europoean Union’s Horizon 2020 research and innovation programme | |
Horizon 2020 Framework Programme | 802774 |
European Commission | |
Planning and Budgeting Committee of the Council for Higher Education of Israel |