Natural Language Processing · TEE · papers
Q4b · Data acquisition and four augmentation methods
2024-12-03 · final · recovered AI paper
Previous examination · complete worked answers
Answer
Data acquisition obtains language examples relevant to the task from existing data, public resources or appropriate new collection. Product instrumentation can gather useful feedback. Supervised learning additionally needs an annotation process when labels are absent.
Data augmentation creates training variants intended to preserve the correct label. It is not simply changing words arbitrarily.
| Method | Procedure | Caution |
|---|---|---|
| Synonym replacement | Use suitable synonyms for selected words | Choose the right sense |
| Back translation | Translate to another language and back | May change meanings/entities |
| TF–IDF-informed replacement | Protect informative words while varying others | Do not remove label-bearing clues |
| Bigram flipping | Swap adjacent words | Can break grammar or meaning |
| Entity replacement | Replace by same-type entity | Must preserve the task label |
| Noise injection | Simulate typos/keyboard errors | Match expected noise |
| Active learning | Select informative cases for human labels | This acquires labels; it is not synthetic text |
Three label decisions
“The film is good”→“The film is excellent” may preserve a positive sentiment label. “The film is not good”→“The film is good” reverses the label and is not valid label-preserving augmentation. “Flights to Pune”→“Flights to Mumbai” may preserve a travel-topic label but changes the destination label.
Split train/test data before creating related augmented variants. Otherwise near-duplicates can leak between sets and produce misleading evaluation. Keep a representative evaluation set of genuine examples.
Sentence badal sakta hai; required correct label nahi. Label badla toh augmentation invalid.
| Method | Example | Required label check |
|---|---|---|
| Synonym replacement | good film→excellent film | Replacement must preserve sense and sentiment |
| Back-translation | English→French→English paraphrase | Check negation, entity and factual preservation |
| Contextual paraphrasing | The delivery was late→The package arrived late | Do not change class or named facts |
| Controlled noise / perturbation | Small realistic spelling error or punctuation variation | Appropriate only if target should remain unchanged |
Acquire licensed/public datasets, commissioned annotation, surveys/user-consented logs or permitted scraping. Augment training data only after splitting to avoid near-duplicate leakage into validation/test. More synthetic examples do not guarantee better generalization.