A team is fine-tuning a text classifier but has only 500 labelled training samples. Which data augmentation technique is most likely to preserve the original label while increasing the number of training examples?
- ARandomly shuffling the order of sentences within each document
- BTruncating each document to exactly half its original word count
- CTranslating documents into a different language and discarding originals
- DReplacing selected words with synonyms drawn from a lexical database Correct
Why A is wrong: Shuffling sentences is tempting because it creates new document orderings, but it disrupts coherence and often changes the meaning enough to corrupt the original label, making it unreliable for classification tasks.
Why B is wrong: Truncation is tempting as a quick resize step, but removing half the content frequently drops sentiment cues or key facts, altering what the label should be and reducing information available to the model.
Why C is wrong: Translation alone changes the language domain, and discarding originals removes the source-language distribution the classifier needs; this is not standard augmentation but rather domain shift.
Why D is correct: Synonym replacement substitutes words with semantically equivalent alternatives, keeping the overall meaning and therefore the class label intact while producing a distinct token sequence the model has not seen before.