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Recent approaches have exploited weaknesses in monolingual question answering(QA) models by adding adversarial statements to the passage. These attackscaused a reduction in state-of-the-art performance by almost 50%. In thispaper, we are the first to explore and successfully attack a multilingual QA(MLQA) system pre-trained on multilingual BERT using several attack strategiesfor the adversarial statement reducing performance by as much as 85%. We showthat the model gives priority to English and the language of the questionregardless of the other languages in the QA pair. Further, we also show thatadding our attack strategies during training helps alleviate the attacks.

Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering