Learning from Rules Generalizing Labeled Exemplars

By Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi. 2020

94 citations recorded by the source corpus.

In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar model for collecting human supervision to combine the scalability 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. 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.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data (2021, background): “, 2020), to automatically label data for data augmentation (Hancock et al., 2018; Wang et al., 2019a; Awasthi et al., 2020), as additional supervision (Narang et al.”
A Survey on Programmatic Weak Supervision (2022, background): “For example, in text applications, users write keyword-or regex-based LFs that assign corresponding labels to the data points that contains the keyword or matches the speciﬁed regular expression [ Ratner et al. , 2017; Meng et al. , 2018; Awasthi et al. , 2020 ] .”
DataComp: In search of the next generation of multimodal datasets (2023, background): “Classic approaches include distant supervision [61], crowd-sourced labels [145], heuristic rules [5] and feature annotation [89], among others.”