text: 36,113,1258034187107434496,48329145,Thomas Kober,"New paper announcement: ""Data Augmentation for Hypernymy Detection"" with brilliant collaborators Julie Weeds, Lorenzo Bertolini and David Weir @SussexUni arXiv: <LINK> pdf: <LINK> 1/10 We do data augmentation based on distributional composition and GANs and see some solid improvements for basic LR and FF models, with two different distributional vector space models, on supervised hypernymy detection.  2/10 We compare augmentation by composition and GANs to two ways of _extending_ a given training set with either hyponym-hypernym pairs from WordNet or extracted from a large corpus with Hearst Patterns. 3/10 We expected that extending a dataset with pairs from WordNet will likely be the upper limit of what we can expect from our data augmentation techniques, however we find that augmentation by composition and GANs frequently performs better than WordNet 😱😱😱 4/10 Distributional composition: Given t hyponym-hypernym pair dog-animal in the training data, we collect modifiers for animal and dog (e.g. small, hungry, etc) and add small dog-dog, hungry dog-dog, small dog-animal, and hungry dog-animal as additional pairs to the training set.5/10 We average the vector representations for ""hungry"" and ""dog"" to get a composed vector. Negative examples are created by pairing ""small dog"" with a neighbour of ""animal"", say ""vehicle"", such that the negative pair ""small dog-vehicle"" is added to the training data. 6/10 For the GAN based approach - GANDALF (GAN-based Data Augmentation for Lexical inFerence) - we simply aim to generate vectors that ""look like"" real nouns (i.e. that are close in terms of cosine similarity to actual word vectors). 7/10 Given the pair dog-animal, we simply pick the top n most similar GANDALF-ed vectors to dog and animal and add those as additional positive examples to the training data. Negative examples are created by mimicking observed negative examples, e.g. dog-cat. 8/10 We also introduce a new hand annotated dataset - HP4K - that does not rely on WordNet or other hand curated lexicons. Given that some datasets as well as models make use of WordNet, we think that this dataset will be a neat addition to existing test suites. 9/10 Relevant links: arXiv: <LINK> pdf: <LINK> github: <LINK> The dataset is already on github, the code will follow soon. 10/10",https://arxiv.org/abs/2005.01854,"The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-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 dog - dog"" 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 WordNet. 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. ",Data Augmentation for Hypernymy Detection,10,"['New paper announcement: ""Data Augmentation for Hypernymy Detection"" with brilliant collaborators Julie Weeds, Lorenzo Bertolini and David Weir @SussexUni\n\narXiv: <LINK>\npdf: <LINK>\n\n1/10', 'We do data augmentation based on distributional composition and GANs and see some solid improvements for basic LR and FF models, with two different distributional vector space models, on supervised hypernymy detection. \n\n2/10', 'We compare augmentation by composition and GANs to two ways of _extending_ a given training set with either hyponym-hypernym pairs from WordNet or extracted from a large corpus with Hearst Patterns.\n\n3/10', 'We expected that extending a dataset with pairs from WordNet will likely be the upper limit of what we can expect from our data augmentation techniques, however we find that augmentation by composition and GANs frequently performs better than WordNet 😱😱😱\n\n4/10', 'Distributional composition: Given t hyponym-hypernym pair dog-animal in the training data, we collect modifiers for animal and dog (e.g. small, hungry, etc) and add small dog-dog, hungry dog-dog, small dog-animal, and hungry dog-animal as additional pairs to the training set.5/10', 'We average the vector representations for ""hungry"" and ""dog"" to get a composed vector. Negative examples are created by pairing ""small dog"" with a neighbour of ""animal"", say ""vehicle"", such that the negative pair ""small dog-vehicle"" is added to the training data.\n\n6/10', 'For the GAN based approach - GANDALF (GAN-based Data Augmentation for Lexical inFerence) - we simply aim to generate vectors that ""look like"" real nouns (i.e. that are close in terms of cosine similarity to actual word vectors).\n\n7/10', 'Given the pair dog-animal, we simply pick the top n most similar GANDALF-ed vectors to dog and animal and add those as additional positive examples to the training data. Negative examples are created by mimicking observed negative examples, e.g. dog-cat.\n\n8/10', 'We also introduce a new hand annotated dataset - HP4K - that does not rely on WordNet or other hand curated lexicons. Given that some datasets as well as models make use of WordNet, we think that this dataset will be a neat addition to existing test suites.\n\n9/10', 'Relevant links:\narXiv: https://t.co/15L2JgZSfA\npdf: https://t.co/ZXimmHwtrb\ngithub: https://t.co/anC6vR9mlr\n\nThe dataset is already on github, the code will follow soon.\n\n10/10']",20,05,2304