Data Organization | Stanford CS224U Natural Language Understanding | Spring 2021 | Summary and Q&A

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January 7, 2022
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Data Organization | Stanford CS224U Natural Language Understanding | Spring 2021

TL;DR

Data sets in NLP are commonly organized using train-dev-test splits, but using predefined splits can limit true progress and new benchmark tasks should be set. For small data sets, imposing a split or using cross-validation can facilitate robust comparisons.

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Questions & Answers

Q: Why are train-dev-test splits commonly used in NLP data sets?

Train-dev-test splits are used to ensure consistency in evaluations across different systems and researchers. By setting aside separate data for training, development, and testing, it allows for fair comparisons.

Q: What is the downside of using pre-defined train-dev-test splits?

The main downside is that everyone using the same splits can lead to mistaken progress. Over time, as progress is made on a benchmark task using the same test set, it becomes difficult to gauge true progress on the underlying task.

Q: How can new benchmark tasks help combat the issue of mistaken progress?

Setting new benchmark tasks with new test sets allows for evaluation in truly unseen environments. It helps to ensure that progress is genuinely made on the underlying task rather than just optimizing for a specific test set.

Q: What challenges arise when dealing with small public data sets without predefined splits?

Small data sets without predefined splits make it difficult to compare results. Differences in assessment regimes, such as train-test splits, can introduce variances that hinder robust comparisons.

Q: How can imposing a split at the start of a project simplify the experimental setup?

Imposing a split at the start of a project simplifies the experimental setup by reducing the number of moving parts. With a predefined split, there is less need for hyperparameter optimization, resulting in a simpler setup.

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

Cross-validation involves partitioning the data into multiple train-test splits and averaging evaluation results. It is used for small data sets to mitigate variance and provide a more reliable measure of system performance.

Q: What is the difference between random splits and k-folds cross-validation?

Random splits involve shuffling the data and splitting it into train and test sets, while k-folds cross-validation divides the data into multiple folds for training and testing. Random splits allow for flexibility in the ratio of train-test examples, while k-folds ensures every example appears in the train set k-1 times.

Q: What are the trade-offs between random splits and k-folds cross-validation?

Random splits offer the freedom to run multiple experiments with a fixed train-test ratio but may introduce unwanted correlations for small data sets. K-folds cross-validation guarantees every example appears in the train set k-1 times, but the size of k determines the size of the train-test split.

Summary & Key Takeaways

  • Train-dev-test splits are commonly used in NLP data sets to ensure consistency in evaluations, but they can limit progress and make it difficult to assess true generalization.

  • Predefined splits in popular test sets can lead to mistaken progress, and setting new benchmark tasks with new test sets is necessary to combat this.

  • Small data sets without predefined splits pose methodological challenges, and running models with the same splits is important for reliable comparisons. Imposing a split can simplify experimental setups, but for highly variable performance, cross-validation can be used.

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