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Multi-Label Text Classification of Derived Text Formats with DistilBERT

Derived Text Formats enable the distribution of copyrighted texts by systematically perturbing linguistic information to reduce reconstructability. However, the extent to which such information loss affects downstream text classification remains unclear. We investigate how controlled perturbations affect learning dynamics in transformer-based classification using two datasets and two strategies: POS-consistent replacement of 30%, 40%, and 50% of tokens, and random word-order shuffling. On Wikipedia data, POS replacement increases loss by 4-9% and reduces micro-F1 by 3-8%, depending on the replacement rate, while shuffling raises loss by 5% and lowers micro-F1 by 4%. Performance degrades monotonically with higher replacement rates, and shuffling yields results between the 30% and 40% conditions, indicating that DistilBERT relies more on lexical semantics than on word order. Experiments on specialist-domain data show the same pattern, demonstrating robustness across domains. To test cross-representation generalization, we train classifiers on both clean and perturbed texts and evaluate them on the respective alternate representation. Models trained on DTF data generalize better to clean text than vice versa, suggesting that perturbation-based training promotes more robust representations. Our findings position DTF as a promising strategy for reproducible, legally compliant, and robust NLP research.