Zero-shot Learning of Classifiers from Natural Language Quantification

By Shashank Srivastava, Igor Labutov, Tom Michael Mitchell. 2018

59 citations recorded by the source corpus.

Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic quantifiers (e.g., ‘usually’ vs ‘always’) to drive model training. Experiments on three domains show that the learned classifiers outperform previous approaches for learning with limited data, and are comparable with fully supervised classifiers trained from a small number of labeled examples.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, background): “Our work also falls in the well-established category of zero-shot learning, which has historically been used to refer to classifying instances among a set of unseen categories (Lampert et al., 2009; Romera-Paredes & Torr, 2015; Srivastava et al., 2018; Yin et al., 2019, inter alia).”
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data (2021, methodology): “One natu- ral way to use explanations is to constrain the learned model, e.g. by constraining the conditional distributions the function can express (Srivastava et al., 2017; 2018), or through placing priors over how features are weighted or extracted (Zaidan et al., 2007; Small et al., 2011; Zhang…”
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (2019, background): “Srivastava et al. (2018) assume that some natural language explanations about new labels are available.”
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback (2020, background): “) classifiers (Srivastava et al., 2018), etc.”
Why Build an Assistant in Minecraft? (2019, background): “[76] Shashank Srivastava, Igor Labutov, and Tom Mitchell.”