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A Latent-Variable Model for Intrinsic Probing ...
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Abstract:
The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information. Indeed, it is natural to assume that these pre-trained representations do encode some level of linguistic knowledge as they have brought about large empirical improvements on a wide variety of NLP tasks, which suggests they are learning true linguistic generalization. In this work, we focus on intrinsic probing, an analysis technique where the goal is not only to identify whether a representation encodes a linguistic attribute, but also to pinpoint where this attribute is encoded. We propose a novel latent-variable formulation for constructing intrinsic probes and derive a tractable variational approximation to the log-likelihood. Our results show that our model is versatile and yields tighter mutual information estimates than two intrinsic probes previously proposed in the literature. Finally, we find empirical evidence that pre-trained representations develop a ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2201.08214
https://dx.doi.org/10.48550/arxiv.2201.08214
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| 9 |
Finding Concept-specific Biases in Form--Meaning Associations ...
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| 10 |
Searching for Search Errors in Neural Morphological Inflection ...
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| 11 |
Applying the Transformer to Character-level Transduction ...
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Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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| 17 |
Examining the Inductive Bias of Neural Language Models with Artificial Languages ...
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