Neuro-Symbolic Entropy Regularization
Kareem Ahmed, Eric Wang, Kai-Wei Chang, and Guy Van den Broeck, in UAI, 2022.
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Abstract
In structured prediction, the goal is to jointly predict many output variables that together encode a structured object – a path in a graph, an entity-relation triple, or an ordering of objects. Such a large output space makes learning hard and requires vast amounts of labeled data. Different approaches leverage alternate sources of supervision. One approach – entropy regularization – posits that decision boundaries should lie in low-probability regions. It extracts supervision from unlabeled examples, but remains agnostic to the structure of the output space. Conversely, neuro-symbolic approaches exploit the knowledge that not every prediction corresponds to a valid structure in the output space. Yet, they does not further restrict the learned output distribution. This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object. It is obtained by restricting entropy regularization to the distribution over only valid structures. This loss is efficiently computed when the output constraint is expressed as a tractable logic circuit. Moreover, it seamlessly integrates with other neuro-symbolic losses that eliminate invalid predictions. We demonstrate the efficacy of our approach on a series of semi-supervised and fully-supervised structured-prediction experiments, where we find that it leads to models whose predictions are more accurate and more likely to be valid.
Bib Entry
@inproceedings{ahmadneuro2022,
title = {Neuro-Symbolic Entropy Regularization},
author = {Ahmed, Kareem and Wang, Eric and Chang, Kai-Wei and den Broeck, Guy Van},
booktitle = {UAI},
year = {2022}
}
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Neuro-Symbolic Entropy Regularization
Kareem Ahmed, Eric Wang, Kai-Wei Chang, and Guy Van den Broeck, in UAI, 2022.
Full Text Abstract BibTeX DetailsDetailsIn structured prediction, the goal is to jointly predict many output variables that together encode a structured object – a path in a graph, an entity-relation triple, or an ordering of objects. Such a large output space makes learning hard and requires vast amounts of labeled data. Different approaches leverage alternate sources of supervision. One approach – entropy regularization – posits that decision boundaries should lie in low-probability regions. It extracts supervision from unlabeled examples, but remains agnostic to the structure of the output space. Conversely, neuro-symbolic approaches exploit the knowledge that not every prediction corresponds to a valid structure in the output space. Yet, they does not further restrict the learned output distribution. This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object. It is obtained by restricting entropy regularization to the distribution over only valid structures. This loss is efficiently computed when the output constraint is expressed as a tractable logic circuit. Moreover, it seamlessly integrates with other neuro-symbolic losses that eliminate invalid predictions. We demonstrate the efficacy of our approach on a series of semi-supervised and fully-supervised structured-prediction experiments, where we find that it leads to models whose predictions are more accurate and more likely to be valid.
@inproceedings{ahmadneuro2022, title = {Neuro-Symbolic Entropy Regularization}, author = {Ahmed, Kareem and Wang, Eric and Chang, Kai-Wei and den Broeck, Guy Van}, booktitle = {UAI}, year = {2022} }