论文标题
DADC 2022的Longhorns：愚弄一个问题回答模型需要多少语言学家？对抗攻击的系统方法
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
论文作者
论文摘要
开发对手挑战NLP系统的方法是提高模型性能和解释性的有前途的途径。在这里，我们描述了团队在第一个动态对抗数据收集（DADC）的任务1中“长角牛”的方法，该工作室要求团队手动欺骗一个模型，以挖掘出挖掘的问题回答任务。我们的团队首先结束，模型错误率为62％。我们主张采用系统的，语言知情的方法来制定对抗性问题，并描述了试点实验的结果以及我们的官方提交。
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team "longhorns" on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first, with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.
