Read + Verify: Machine Reading Comprehension with Unanswerable Questions

Understand

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.

• In addition to extract answers, previous works usually predict an additional "no-answer" probability to detect unanswerable cases.
• However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer.
• To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets.