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

本文介绍我们在创建高质量的、针对任务 1 的机器阅读理解数据的过程中的经验，并描述了我们基于不同专题的定量定性分析结果以及与注释员信心的相关性，最后我们提出了相关建议。