论文标题
用于产生深层问题的语义图
Semantic Graphs for Generating Deep Questions
论文作者
论文摘要
本文提出了深度问题生成（DQG）的问题，该问题旨在产生复杂的问题，这些问题需要在输入段落的多个信息上进行推理。为了捕获文档的全局结构并促进推理，我们提出了一个新颖的框架，该框架首先构造了输入文档的语义级别图，然后通过引入基于注意力的GGNN（ATT-GGGNN）来编码语义图。之后，我们融合文档级别和图形表示形式，以执行内容选择和问题解码的联合培训。在HotPotQA深度问题中心数据集上，我们的模型大大提高了需要对多个事实进行推理的问题的绩效，从而导致了最新的性能。该代码可在https://github.com/wing-nus/sg-deep-question-generation上公开获取。
This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework which first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterwards, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at https://github.com/WING-NUS/SG-Deep-Question-Generation.
