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We need to talk about standard splits.

Kyle Gorman1, Steven Bedrick2

  • 1City University of New York.

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|June 23, 2023
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Summary
This summary is machine-generated.

System rankings in speech and language technology are often unreliable. Using randomly generated data splits is crucial for robustly evaluating part-of-speech tagger performance and ensuring reproducible results.

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Area of Science:

  • Speech and Language Technology
  • Computational Linguistics
  • Natural Language Processing

Background:

  • Standard practice involves evaluating systems on held-out test sets.
  • Statistical significance and ranking stability are often overlooked in system comparisons.
  • State-of-the-art claims are frequently based on specific, non-randomized data splits.

Purpose of the Study:

  • To investigate the reliability of system rankings in speech and language technology.
  • To assess the impact of different data splitting strategies on evaluation outcomes.
  • To propose best practices for reproducible system comparisons.

Main Methods:

  • Conducted replication and reproduction experiments on nine part-of-speech taggers.
  • Evaluated systems using both a widely-used 'standard split' and randomly generated splits.
  • Compared system performance rankings across different experimental setups.

Main Results:

  • System rankings were not reliably reproduced when using randomly generated splits.
  • The stability of rankings varied significantly depending on the data splitting method.
  • Some state-of-the-art claims based on standard splits were questionable under random splitting.

Conclusions:

  • The use of standard, non-randomized splits may lead to unreliable system comparisons.
  • Randomly generated splits are recommended for more robust and reproducible evaluation of speech and language technology systems.
  • Future research should prioritize methods ensuring the stability and reproducibility of system rankings.