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In the era of deep learning, the predict-then-refine paradigm is commonly applied to sequence-to-sequence problems. Tasks such as grammar correction and text rewriting often follow this theme, where an initial sequence is generated and then refined to improve its quality and correctness.

Modern NLP Applications of the Predict-then-Refine Paradigm

An engineering team is building a system to summarize long technical documents. They are considering several architectures. Which of the following designs best exemplifies a 'predict-then-refine' approach for a sequence-to-sequence task?

A tech company has built a neural machine translation system that translates English text to Spanish. While the system quickly generates translations that capture the general meaning, users complain about frequent grammatical errors and unnatural-sounding sentences. Based on your understanding of common paradigms for sequence-to-sequence tasks, propose and describe a two-stage architectural modification that could address this issue without retraining the initial translation model from scratch.

Improving a Machine Translation System

A team is developing a feature to rewrite informal user-generated text into a more formal style. They are considering two deep learning-based approaches: 

1. A single, large sequence-to-sequence model trained end-to-end to directly transform informal text to formal text. 
2. A two-stage system where a first model generates an initial formal version ('predict'), and a second, specialized model corrects grammatical errors and improves the stylistic formality of the initial output ('refine'). 

Evaluate the two-stage 'predict-then-refine' approach compared to the single-model approach for this specific task. Discuss the potential trade-offs, including aspects like output quality, computational cost, and system complexity.

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