论文标题
基于深刻理解的多档案机理解理解
Deep Understanding based Multi-Document Machine Reading Comprehension
论文作者
论文摘要
大多数现有的多档案机读取理解模型主要集中在理解输入问题和文档之间的相互作用，但经过两种理解后忽略。首先，从彼此的角度了解输入问题和文档中单词的语义含义。其次，从文档内和文档间的角度了解正确答案的支持提示。忽略这两种重要理解将使模型监督一些重要信息，这些信息可能有助于指示正确的答案。为了克服这一效率，我们为多档案机读取理解理解提供了一个基于深刻理解的模型。它具有三个级联的深度理解模块，旨在了解单词的准确语义含义，输入问题和文档之间的相互作用以及正确答案的支持提示。我们在两个大型基准数据集上评估了我们的模型，即Triviaqa Web和Dureader。广泛的实验表明，我们的模型在两个数据集上都达到了最新的结果。
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each other. Second, to understand the supporting cues for a correct answer from the perspective of intra-document and inter-documents. Ignoring these two kinds of important understandings would make the models oversee some important information that may be helpful for inding correct answers. To overcome this deiciency, we propose a deep understanding based model for multi-document machine reading comprehension. It has three cascaded deep understanding modules which are designed to understand the accurate semantic meaning of words, the interactions between the input question and documents, and the supporting cues for the correct answer. We evaluate our model on two large scale benchmark datasets, namely TriviaQA Web and DuReader. Extensive experiments show that our model achieves state-of-the-art results on both datasets.
