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Heavy-tailed Representations, Text Polarity Classification & Data Augmentation

Abstract : 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. In particular, a classifier dedicated to the tails of the proposed embedding is obtained which exhibits a scale invariance property exploited in a novel text generation method for label preserving dataset augmentation. Experiments on synthetic and real text data show the relevance of the proposed framework and confirm that this method generates meaningful sentences with controllable attribute, e.g. positive or negative sentiments.
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Preprints, Working Papers, ...
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Contributor : Hamid Jalalzai Connect in order to contact the contributor
Submitted on : Friday, September 11, 2020 - 3:07:02 PM
Last modification on : Thursday, October 7, 2021 - 3:12:41 AM


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  • HAL Id : hal-02936647, version 1
  • ARXIV : 2003.11593


Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Éric Gaussier, Giovanna Varni, et al.. Heavy-tailed Representations, Text Polarity Classification & Data Augmentation. 2020. ⟨hal-02936647⟩



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