Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Natural Language Generation | Summary and Q&A

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October 29, 2021
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Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Natural Language Generation

TL;DR

Evaluation and training in natural language generation involve using automatic metrics like content overlap and model-based metrics, as well as human evaluations to assess the quality and effectiveness of the generated text.

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Questions & Answers

Q: What are content overlap metrics?

Content overlap metrics compare the generated text with a reference sequence using word or phrase matching to measure their similarity.

Q: What are model-based metrics?

Model-based metrics use embeddings and neural models to define implicit similarity measures between sequences, going beyond simple word or phrase matching.

Q: Why are human evaluations considered the gold standard?

Human evaluations involve human assessors who rate the quality, fluency, and coherence of the generated text, providing a more comprehensive and nuanced assessment compared to automatic metrics.

Q: What are the benefits of content overlap metrics?

Content overlap metrics are fast and efficient, allowing for rapid evaluation and feedback on the generated text.

Summary & Key Takeaways

  • Content overlap metrics focus on measuring the similarity between the generated text and a reference sequence, using word or phrase matching.

  • Model-based metrics use advances in embeddings and neural models to define implicit similarity measures between sequences.

  • Human evaluations are considered the gold standard and involve human assessors rating the quality and fluency of the generated text.

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