We present a deep generative model of question-answer (QA) pairs for machine reading comprehension. We introduce two independent latent random variables into our model in order to diversify answers and questions separately. We also study the effect of explicitly controlling the KL term in the variational lower bound in order to avoid the "posterior collapse" issue, where the model ignores latent variables and generates QA pairs that are almost the same. Our experiments on SQuAD v1.1 showed that variational methods can aid QA pair modeling capacity, and that the controlled KL term can significantly improve diversity while generating high-quality questions and answers comparable to those of the existing systems.

中文翻译：

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机器阅读理解的变分问答对生成

我们提出了一个用于机器阅读理解的问答（QA）对的深度生成模型。我们在我们的模型中引入了两个独立的潜在随机变量，以便分别使答案和问题多样化。我们还研究了在变分下界中明确控制 KL 项的效果，以避免“后崩溃”问题，其中模型忽略潜在变量并生成几乎相同的 QA 对。我们在 SQuAD v1.1 上的实验表明，变分方法可以帮助 QA 对建模能力，并且受控 KL 项可以显着提高多样性，同时生成与现有系统相当的高质量问题和答案。