@inproceedings{kovatchev-etal-2022-longhorns,
    title = "longhorns at {DADC} 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.",
    author = "Kovatchev, Venelin  and
      Chatterjee, Trina  and
      Govindarajan, Venkata S  and
      Chen, Jifan  and
      Choi, Eunsol  and
      Chronis, Gabriella  and
      Das, Anubrata  and
      Erk, Katrin  and
      Lease, Matthew  and
      Li, Junyi Jessy  and
      Wu, Yating  and
      Mahowald, Kyle",
    booktitle = "Proceedings of the First Workshop on Dynamic Adversarial Data Collection",
    month = jul,
    year = "2022",
    address = "Seattle, WA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.dadc-1.5",
    doi = "10.18653/v1/2022.dadc-1.5",
    pages = "41--52",
    abstract = "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 (pending validation), 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.",
}