ZeroBERTo -- Leveraging Zero-Shot Text Classification by Topic Modeling

• URL: http://arxiv.org/abs/2201.01337v1
• Date: Tue, 4 Jan 2022 20:08:17 GMT
• Title: ZeroBERTo -- Leveraging Zero-Shot Text Classification by Topic Modeling
• Authors: Alexandre Alcoforado, Thomas Palmeira Ferraz, Rodrigo Gerber, Enzo Bustos, Andr\'e Seidel Oliveira, Bruno Miguel Veloso, Fabio Levy Siqueira, Anna Helena Reali Costa
• Abstract summary: 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.
• Score: 57.80052276304937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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