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Most NLP datasets are manually labeled, so suffer from inconsistent labelingor limited size. We propose methods for automatically improving datasets byviewing them as graphs with expected semantic properties. We construct aparaphrase graph from the provided sentence pair labels, and create anaugmented dataset by directly inferring labels from the original sentence pairsusing a transitivity property. We use structural balance theory to identifylikely mislabelings in the graph, and flip their labels. We evaluate ourmethods on paraphrase models trained using these datasets starting from apretrained BERT model, and find that the automatically-enhanced training setsresult in more accurate models.

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