Adversarial TableQA: Attention Supervision for Question Answering on Tables

Understand

The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets.

• Recently, there have been doubts whether such rapid progress has been based on truly understanding language.
• The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table.
• We show that existing efforts, of using "answers" for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer.