Generating artificial texts as substitution or complement of training data
This addresses the problem of data scarcity and confidentiality in NLP tasks, but it is incremental as it builds on existing transformer-based generation methods.
The study investigated whether artificially generated texts from fine-tuned GPT-2 models can serve as a complement or substitute for training data in supervised learning tasks like sentiment analysis and fake news detection, finding that such data can be used to some extent but requires pre-processing to significantly improve performance, with bag-of-words approaches benefiting the most.
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.