Heavy-tailed Representations, Text Polarity Classification & Data Augmentation

25 Mar 2020Hamid JalalzaiPierre ColomboChloé ClavelEric GaussierGiovanna VarniEmmanuel VignonAnne Sabourin

The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation. In this paper, we develop a novel method to learn a heavy-tailed embedding with desirable regularity properties regarding the distributional tails, which allows to analyze the points far away from the distribution bulk using the framework of multivariate extreme value theory... (read more)

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