《Table 1 Examples of SQu AD 2.0》

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《通过检测语义分歧识别无答案问题（英文）》

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Machine reading comprehension（MRC）is a kind of question answering system based on the facts in the reference text.It has received considerable attention over the past few years.With the benefit of the first high-quality and large MRC dataset SQuAD 1.1[1]，MRC models with deep learning architectures are proposed and have achieved promising results on a variety of tasks.However，most of them are trained to choose the most probable answer by comparing the candidate answers under the hypothesis that the given text always has the correct answer in its context.However，this hypothesis cannot be guaranteed in real world，some questions might be unanswerable only by its reference text.SQuAD 2.0[2]released recently offers a no-answer（NA）option to each question.Table 1 gives an example in SQu AD 2.0 which cannot be answered from the given reference text.The unanswerable questions in SQuAD 2.0 are written specially to be similar to answerable ones，all of the questions’contents are relevant to the passage and each of the unanswerable questions is provided a plausible answer which is not real.So，there are no obvious differences between answerable questions and unanswerable questions，and they must be distinguished by deep semantic matching.