论文标题
Gennet：使用生成和选择模型的多项选择问题阅读理解
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model
论文作者
论文摘要
多选择机器阅读理解是艰巨的任务，因为其所需的机器可以使用给定的段落和问题从一组候选人或可能的选项中选择正确的选项。阅读具有多项选择问题任务的理解，需要人（或机器）来读取给定段落，问题对，并从n个给定选项中选择最佳的一个选项。从给定段落中选择正确答案的方法有两种不同。通过消除最坏的匹配答案来选择最佳匹配答案。在这里，我们提出了基于神经网络的模型Gennet模型。首先，在此模型中，我们将从段落中生成问题的答案，然后将生成的答案与给定的答案匹配，最佳匹配选项将是我们的答案。对于答案生成，我们使用了S-NET（Tan等，2017）模型，该模型在小队上训练并评估我们使用大型种族的模型（从考试中阅读理解数据集）（Lai等，2017）。
Multiple-choice machine reading comprehension is difficult task as its required machines to select the correct option from a set of candidate or possible options using the given passage and question.Reading Comprehension with Multiple Choice Questions task,required a human (or machine) to read a given passage, question pair and select the best one option from n given options. There are two different ways to select the correct answer from the given passage. Either by selecting the best match answer to by eliminating the worst match answer. Here we proposed GenNet model, a neural network-based model. In this model first we will generate the answer of the question from the passage and then will matched the generated answer with given answer, the best matched option will be our answer. For answer generation we used S-net (Tan et al., 2017) model trained on SQuAD and to evaluate our model we used Large-scale RACE (ReAding Comprehension Dataset From Examinations) (Lai et al.,2017).
