  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. Moreover, we introduce two auxiliary losses to
help the reader better handle answer extraction as well as no-answer detection,
and investigate three different architectures for the answer verifier. Our
experiments on the SQuAD 2.0 dataset show that our system achieves a score of
74.2 F1 on the test set, achieving state-of-the-art results at the time of
submission (Aug. 28th, 2018).

Computation and Language