Probing as Quantifying the Inductive Bias of Pre-trained Representations

• URL: http://arxiv.org/abs/2110.08388v1
• Date: Fri, 15 Oct 2021 22:01:16 GMT
• Title: Probing as Quantifying the Inductive Bias of Pre-trained Representations
• Authors: Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
• Abstract summary: We present a novel framework for probing where the goal is to evaluate the inductive bias of representations for a particular task. We apply our framework to a series of token-, arc-, and sentence-level tasks.
• Score: 99.93552997506438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained contextual representations have led to dramatic performance improvements on a range of downstream tasks. This has motivated researchers to quantify and understand the linguistic information encoded in them. In general, this is done by probing, which consists of training a supervised model to predict a linguistic property from said representations. Unfortunately, this definition of probing has been subject to extensive criticism, and can lead to paradoxical or counter-intuitive results. In this work, we present a novel framework for probing where the goal is to evaluate the inductive bias of representations for a particular task, and provide a practical avenue to do this using Bayesian inference. We apply our framework to a series of token-, arc-, and sentence-level tasks. Our results suggest that our framework solves problems of previous approaches and that fastText can offer a better inductive bias than BERT in certain situations.

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