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首页> 外文会议>Annual meeting of the Association for Computational Linguistics >Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text
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Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

机译：推理机理解：通过从文本中递归推论证据链来回答问题

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This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. In particular, we first encode the given document, question and options in a context aware way. We then propose a new network to solve the inference problem by decomposing it into a series of attention-based reasoning steps. The result of the previous step acts as the context of next step. To make each step can be directly inferred from the text, we design an operational cell with prior structure. By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells. Moreover, a termination mechanism is introduced to dynamically determine the uncertain reasoning depth, and the network is trained by reinforcement learning. Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach.
机译：本文着重于推理机理解这一主题，其目的是充分理解给定文本的含义，以回答一般性问题，尤其是那些需要推理能力的问题。特别是，我们首先以上下文感知的方式对给定的文档，问题和选项进行编码。然后，我们提出了一个新的网络，通过将其分解为一系列基于注意力的推理步骤来解决推理问题。上一步的结果将作为下一步的上下文。为了使每个步骤都可以从文本中直接推断出来，我们设计了一个具有先验结构的运算单元。通过递归链接这些单元，可以将推断的结果综合在一起以形成推理的证据链，其中可以通过施加结构约束来调节在单元上的交互作用来指导推理方向。此外，引入了一种终止机制来动态确定不确定的推理深度，并通过强化学习来训练网络。在3种流行数据集（包括MCTest，RACE和MultiRC）上的实验结果证明了我们方法的有效性。

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