Read + Verify: Machine Reading Comprehension with Unanswerable Questions

17 Aug 2018  ·  Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li ·

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).

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering SQuAD2.0 Reinforced Mnemonic Reader + Answer Verifier (single model) EM 71.767 # 235
F1 74.295 # 243
Question Answering SQuAD2.0 dev RMR + ELMo (Model-III) F1 74.8 # 12
EM 72.3 # 11


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