Back-translation augments data by translating text to another language and back to create paraphrased versions. Process: Original text → translate to language B → translate back to original language → paraphrased version. Translation model introduces variations. Why it works: Intermediate language forces different word choices, sentence structures while preserving meaning. Example: "The cat sat on the mat" → French: "Le chat s'est assis sur le tapis" → back: "The cat sat down on the carpet". Implementation: Use translation APIs (Google Translate, DeepL) or neural MT models, chain translations through one or more pivot languages. Enhancement strategies: Use multiple pivot languages for more diversity, filter low-quality paraphrases, combine with other augmentation. Quality considerations: May introduce errors, check semantic preservation, some sentences augment better than others. Use cases: Low-resource languages, text classification, question answering, semantic similarity training, instruction tuning data. Trade-offs: API costs, translation model quality matters, computational overhead. Simple but effective technique; widely used in industry and research.