Generating artificial texts as substitution or complement of training data

TL;DR

This paper investigates the use of transformer-generated artificial texts as data augmentation, replacement, or explainability tools in supervised learning tasks like sentiment analysis and Fake News detection, highlighting their benefits and limitations.

Contribution

It explores the effectiveness of GPT-2 generated data for augmentation, replacement, and explainability in Web-related classification tasks, with specific focus on data pre-processing and bag-of-words methods.

Findings

Artificial data can enhance classifier performance with proper pre-processing.

Bag-of-words approaches benefit most from artificial data augmentation.

Generated data can partially replace original data when unavailable or confidential.

Abstract

The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises. In this article, this question is explored under 3 aspects: (i) are artificial data an efficient complement? (ii) can they replace the original data when those are not available or cannot be distributed for confidentiality reasons? (iii) can they improve the explainability of classifiers? Different experiments are carried out on Web-related classification tasks -- namely sentiment analysis on product reviews and Fake News detection -- using artificially generated data by fine-tuned GPT-2 models. The results show that such artificial data can be used in a certain extend but require pre-processing to significantly improve performance. We show that bag-of-word approaches benefit the most from such data augmentation.