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Explain how end-to-end QA exemplifies directly learning rich outputs.
Question: In the context of Machine Learning Yearning, analyze how end-to-end question answering systems demonstrate the concept of directly learning rich outputs. Be sure to detail the specific inputs and outputs involved in this formulation.
Sample answer: End-to-end question answering exemplifies directly learning rich outputs by mapping a complex, multi-part input directly to a descriptive textual output. Instead of parsing text or relying on manual feature extraction steps, the system takes a (Text, Question) pair as its input. It then directly learns to generate the Answer text as its final output. This single-step mapping from a paired text and question to a final generated answer highlights the capacity of modern end-to-end systems to learn complex, rich outputs directly from data.
Key points:
- The system takes a (Text, Question) pair as input.
- The system produces answer text as its output.
- It directly maps this complex input to the final answer without intermediate extraction steps.
- This direct mapping is a prime example of directly learning rich outputs.
Rubric: The answer should explicitly state that the system takes a text and question pair as input and produces answer text as output, linking this direct mapping to the concept of rich outputs.
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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