Compare RNNs and Transformers for Long-Sequence Text Classification

Q: Compare RNNs and Transformers for Long-Sequence Text Classification

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.

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Question

Scenario

You are designing a long-sequence text classification system under tight inference latency constraints (e.g., large documents or logs that must be classified quickly on GPU/CPU).

Task

Part A: Contrast RNNs and Transformers in terms of architecture, parallelism, context handling, and training dynamics for long-sequence classification with strict latency budgets.
Part B: Describe bagging and boosting ensemble techniques, including their goals (variance vs. bias reduction) and when each is preferable under practical constraints.

Hints

Address attention and parallelism, sequence length limits, and training stability.
For ensembles, discuss variance reduction (bagging) and bias reduction (boosting), latency implications, and practical guardrails.

Compare RNNs and Transformers for Long-Sequence Text Classification

Scenario

Task

Hints

Solution

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