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arXiv · 2201.01337

ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling

Alexandre Alcoforado·Thomas Palmeira Ferraz·Rodrigo Gerber·Enzo Bustos·André Seidel Oliveira·Bruno Miguel Veloso·Fabio Levy Siqueira·Anna Helena Reali Costa
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Abstract

Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages. This lack of labeled data has led to the rise of low-resource methods, that assume low data availability in natural language processing. Among them, zero-shot learning stands out, which consists of learning a classifier without any previously labeled data. The best results reported with this approach use language models such as Transformers, but fall into two problems: high execution time and inability to handle long texts as input. This paper proposes a new model, ZeroBERTo, which leverages an unsupervised clustering step to obtain a compressed data representation before the classification task. We show that ZeroBERTo has better performance for long inputs and shorter execution time, outperforming XLM-R by about 12% in the F1 score in the FolhaUOL dataset. Keywords: Low-Resource NLP, Unlabeled data, Zero-Shot Learning, Topic Modeling, Transformers.

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Alexandre Alcoforado, Thomas Palmeira Ferraz, Rodrigo Gerber, Enzo Bustos, André Seidel Oliveira, Bruno Miguel Veloso, Fabio Levy Siqueira, Anna Helena Reali Costa. 2022-06-04. ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling. https://doi.org/10.1007/978-3-030-98305-5_12

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