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Compare RNNs and Transformers for Long-Sequence Text Classification

AmazonData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

Last updated

Compare RNNs and Transformers for Long-Sequence Text Classification evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Designing an NLP system for long-sequence text classification under tight inference latency constraints.

Question

Contrast RNNs and Transformers in terms of architecture, parallelism, context handling, and training dynamics. Describe bagging and boosting ensemble techniques and when each is preferable.

Hints

Discuss attention, parallelism, sequence length limits, variance reduction in bagging and bias reduction in boosting.