Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory

Understand

Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size.

• We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties.
• We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property.
• We use structural balance theory to identify likely mislabelings in the graph, and flip their labels.