ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling

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Alcoforado, Alexandre ; Ferraz, Thomas Palmeira ; Gerber, Rodrigo ; Bustos, Enzo ; Oliveira, Andre Seidel ; Veloso, Bruno Miguel ; Siqueira, Fabio Levy ; Reali Costa, Anna Helena ; Pinheiro, V ; Gamallo, P ; Amaro, R ; Scarton, C ; Batista, F ; Silva, D ; Magro, C ; Pinto, H

• Document type: Journal article
• Source: COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROPOR 2022; v. 13208, p. 12-pg., 2022-01-01.

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. (AU)

• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program