Generating Semantically Valid Adversarial Questions for TableQA

Understand

Adversarial attack on question answering systems over tabular data (TableQA) can help evaluate to what extent they can understand natural language questions and reason with tables.

• However, generating natural language adversarial questions is difficult, because even a single character swap could lead to huge semantic difference in human perception.
• In this paper, we propose SAGE (Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequence model for TableQA white-box attack.
• To preserve meaning of original questions, we apply minimum risk training with SIMILE and entity delexicalization.