Yating Wu
2022
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Venelin Kovatchev
|
Trina Chatterjee
|
Venkata S Govindarajan
|
Jifan Chen
|
Eunsol Choi
|
Gabriella Chronis
|
Anubrata Das
|
Katrin Erk
|
Matthew Lease
|
Junyi Jessy Li
|
Yating Wu
|
Kyle Mahowald
Proceedings of the First Workshop on Dynamic Adversarial Data Collection
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.
Search
Co-authors
- Venelin Kovatchev 1
- Trina Chatterjee 1
- Venkata S Govindarajan 1
- Jifan Chen 1
- Eunsol Choi 1
- show all...
Venues
- dadc1