Compare RNNs and Transformers for Long-Sequence Text Classification
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### 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.
Quick Answer: This question evaluates understanding of sequence modeling architectures (RNNs vs. Transformers), attention and parallelism, sequence length limits and training dynamics, and ensemble techniques (bagging for variance reduction versus boosting for bias reduction) as applied to long-sequence text classification under strict inference latency constraints. It is commonly asked in the Machine Learning domain to assess model selection and deployment trade-offs, testing both conceptual understanding and practical application related to latency, context handling, and training stability.
