ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling
| Autor: | Alcoforado, Alexandre, Ferraz, Thomas Palmeira, Gerber, Rodrigo, Bustos, Enzo, Oliveira, André Seidel, Veloso, Bruno Miguel, Siqueira, Fabio Levy, Costa, Anna Helena Reali |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | In: Pinheiro V. et al. (eds) Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science, vol 13208. Springer, Cham |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-98305-5_12 |
| Popis: | 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. Comment: Accepted at PROPOR 2022: 15th International Conference on Computational Processing of Portuguese |
| Databáze: | arXiv |
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