Summary of On Efficient and Statistical Quality Estimation For Data Annotation, by Jan-christoph Klie et al.
On Efficient and Statistical Quality Estimation for Data Annotation
by Jan-Christoph Klie, Juan Haladjian, Marc Kirchner, Rahul Nair
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper discusses the importance of high-quality annotated datasets for training and evaluating machine learning models. To ensure reliable quality estimates, good quality management is crucial. However, manually checking all instances can be expensive, so often only subsets are inspected. This approach may lead to imprecise error rate values due to small sample sizes. The authors propose using confidence intervals to determine the minimal sample size needed for estimating annotation error rates and introduce acceptance sampling as an alternative method. They show that acceptance sampling can reduce sample sizes up to 50% while maintaining statistical guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure datasets used to train machine learning models are accurate and reliable. It’s like checking a puzzle book to make sure the answers are correct. If we only check a few pages, we might not get an accurate idea of how many mistakes there are. This can be expensive, so instead of checking everything, we can use special methods to estimate the number of errors. The authors show that one of these methods, called acceptance sampling, can make our estimates more reliable and save us time and money. |
Keywords
» Artificial intelligence » Machine learning
