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We present our experience as annotators in the creation of high-quality,adversarial machine-reading-comprehension data for extractive QA for Task 1 ofthe First Workshop on Dynamic Adversarial Data Collection (DADC). DADC is anemergent data collection paradigm with both models and humans in the loop. Weset up a quasi-experimental annotation design and perform quantitative analysesacross groups with different numbers of annotators focusing on successfuladversarial attacks, cost analysis, and annotator confidence correlation. Wefurther perform a qualitative analysis of our perceived difficulty of the taskgiven the different topics of the passages in our dataset and conclude withrecommendations and suggestions that might be of value to people working onfuture DADC tasks and related annotation interfaces.

Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop