English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Adversarial attack on question answering systems over tabular data (TableQA)can help evaluate to what extent they can understand natural language questionsand reason with tables. However, generating natural language adversarialquestions is difficult, because even a single character swap could lead to hugesemantic difference in human perception. In this paper, we propose SAGE(Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequencemodel for TableQA white-box attack. To preserve meaning of original questions,we apply minimum risk training with SIMILE and entity delexicalization. We useGumbel-Softmax to incorporate adversarial loss for end-to-end training. Ourexperiments show that SAGE outperforms existing local attack models on semanticvalidity and fluency while achieving a good attack success rate. Finally, wedemonstrate that adversarial training with SAGE augmented data can improveperformance and robustness of TableQA systems.

Generating Semantically Valid Adversarial Questions for TableQA