How to Write Data Labeling/Annotation Guidelines
Eugene Yan
The article provides a framework for writing effective data labeling and annotation guidelines by addressing five key questions: why the task matters, what the task is, how to define relevant terms, how annotators should make decisions, and how to perform the task. The author references Google and Bing's search quality rating guidelines as examples, noting that Google's guidelines include definitions for basic terms like "query" and "locale" and provide numerous annotated examples to calibrate reviewers. Consistent annotation depends on clear definitions, detailed decision-making processes, and measuring inter-rater reliability through metrics like Cohen's kappa.
Why it matters
Writing good instructions to achieve high precision and throughput.
