Learning from Rules Generalizing Labeled ExemplarsDownload PDF

Published: 20 Dec 2019, Last Modified: 22 Oct 2023ICLR 2020 Conference Blind SubmissionReaders: Everyone
TL;DR: Coupled rule-exemplar supervision and a implication loss helps to jointly learn to denoise rules and imply labels.
Abstract: In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. The denoised rules and trained model are used jointly for inference. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.
Keywords: Learning from Rules, Learning from limited labeled data, Weakly Supervised Learning
Code: https://github.com/awasthiabhijeet/Learning-From-Rules
Spotlight Video: https://youtu.be/TQfq4YdqG3k
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/arxiv:2004.06025/code)
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