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This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called good-enough example extrapolation (GE3). GE3 is lightweight and has no hyperparameters. Applied to three text classification datasets for various data imbalance scenarios, GE3 improves performance more than upsampling and other hidden-space data augmentation methods.
In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such few-shot cases at test time. A common remedy is to perform data augmentation, such as by du
In the classical synthesis problem, we are given an LTL formula psi over sets of input and output signals, and we synthesize a system T that realizes psi: with every input sequences x, the system associates an output sequence T(x) such that the gener
Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, w
Ontology-based data integration has been one of the practical methodologies for heterogeneous legacy database integrated service construction. However, it is neither efficient nor economical to build the cross-domain ontology on top of the schemas of
Many top-performing image captioning models rely solely on object features computed with an object detection model to generate image descriptions. However, recent studies propose to directly use scene graphs to introduce information about object rela