Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis

• URL: http://arxiv.org/abs/2306.07664v1
• Date: Tue, 13 Jun 2023 10:14:58 GMT
• Title: Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
• Authors: Zhengxiang Shi, Aldo Lipani
• Abstract summary: We evaluate the effectiveness of three different FT methods in conjugation with back-translation across an array of 7 diverse NLP tasks. Our findings reveal that continued pre-training on augmented data can effectively improve the FT performance of the downstream tasks. Our finding highlights the potential of DA as a powerful tool for bolstering LMs' performance.
• Score: 4.771833920251869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP). However, the impact of data augmentation (DA) techniques on the fine-tuning (FT) performance of these LMs has been a topic of ongoing debate. In this study, we evaluate the effectiveness of three different FT methods in conjugation with back-translation across an array of 7 diverse NLP tasks, including classification and regression types, covering single-sentence and sentence-pair tasks. Contrary to prior assumptions that DA does not contribute to the enhancement of LMs' FT performance, our findings reveal that continued pre-training on augmented data can effectively improve the FT performance of the downstream tasks. In the most favourable case, continued pre-training improves the performance of FT by more than 10% in the few-shot learning setting. Our finding highlights the potential of DA as a powerful tool for bolstering LMs' performance.

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