Data Augmentation for Hypernymy Detection
Thomas Kober, Julie Weeds, Lorenzo Bertolini, David James Weir · 2021
The automatic detection of hypernymy relationships represents a challenging problem in NLP.The successful application of stateof-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data.We have developed two novel data augmentation techniques which generate new training examples from existing ones.First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as small dogdog or small dog -animal, for which a hypernymy relationship can be assumed.Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed.We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as Word-Net.Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.