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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：推理机械理解：通过递归地扣除文本的证据链来回答问题

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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：本文重点介绍了推理机器理解的主题，旨在充分了解给定文本回答通用问题的含义，特别是所需技能的含义。特别是，我们首先以语境意识的方式编码给定的文档，问题和选项。然后，我们提出了一种新的网络来解决推理问题，通过将其分解成一系列基于关注的推理步骤。前一步的结果充当下一步的背景。为了使每个步骤可以直接从文本推断出来，我们设计具有现有结构的操作单元。通过递归地连接细胞，将推断结果合成在一起以形成推理的证据链，其中通过施加结构约束来指导推理方向以调节细胞的相互作用。此外，引入了终止机构以动态地确定不确定的推理深度，并且通过加强学习训练网络。在3个流行数据集的实验结果，包括MCTEST，RACE和MULTIRC，展示了我们方法的有效性。

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