Stanford XCS224U: NLU I NLP Methods and Metrics, Part 5: Data Organization I Spring 2023 | Summary and Q&A

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August 17, 2023
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Stanford XCS224U: NLU I NLP Methods and Metrics, Part 5: Data Organization I Spring 2023

TL;DR

This content discusses the importance of data organization in the field of NLP and AI, including the use of train, development, and test sets, and the challenges posed when working with datasets from different fields.

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Key Insights

  • 😫 Fixed train, development, and test sets in NLP and AI enable consistent evaluations and comparisons between models.
  • 😫 Data sets from fields outside of NLP may not have predefined splits, posing challenges for assessment and comparison.
  • 😵 In situations with small data sets, cross-validation, particularly random splits, provides a way to ensure robust evaluations.
  • 👻 Random splits allow for independent control over the number of train and test examples, but introduce randomness in the process.
  • ☠️ K-fold cross-validation ensures every example appears in the train and test sets, but the number of experiments is entwined with the percentage of train and test data.
  • ☠️ Scikit-learn provides reliable utilities for random splits and k-fold cross-validation.
  • 😵 Researchers should consider the specific requirements of their data sets and experimental design to choose the most appropriate cross-validation method.

Transcript

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Questions & Answers

Q: Why do data sets in NLP and AI usually have train, development, and test portions?

Having these fixed test sets ensures consistent evaluations and makes it easier to compare models that were evaluated using the same protocol. However, it also leads to a community-wide performance inflation on the test set.

Q: What challenges are posed when working with data sets from fields other than NLP?

Data sets from fields like Psychology rarely follow the train, development, and test methodology, making it difficult to ensure robust comparisons. Researchers need to think about assessment regimes and finding ways to use the same splits for all experimental runs.

Q: What is cross-validation and why is it useful for small data sets?

Cross-validation involves partitioning a set of examples into train and test splits, running system evaluations, and aggregating scores. It allows for robust comparisons, even with small data sets, by using multiple train and test combinations.

Q: What are the two broad methods of cross-validation?

The first method is random splits, where the data set is shuffled and split into train and test sets multiple times. The second method is k-fold cross-validation, where the data set is divided into k disjoint parts and experiments are conducted with different combinations of train and test folds.

Summary & Key Takeaways

  • The use of train, development, and test sets is common in NLP and AI, ensuring consistent evaluations and allowing for comparisons between models.

  • Data sets from fields outside of NLP may not have predefined splits, creating a challenge for assessment and comparison.

  • For small data sets, cross-validation can be used to partition examples into train and test sets and aggregate scores to measure system performance.

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