论文标题
一个具有事实，时间和逻辑知识的异质图，用于在动态上下文中回答问题
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
论文作者
论文摘要
我们研究了关于动态文本环境的问题。尽管神经网络模型通过从投入输出示例中学习实现了令人印象深刻的准确性，但它们很少利用各种类型的知识，并且通常不可解释。在这项工作中，我们提出了一种基于图形的方法，其中一个异质图是通过对上下文的事实知识，对过去状态的时间知识以及结合了人类策划的知识基础和规则基础的逻辑知识的自动构建的。我们通过构造的图开发图形神经网络，并以端到端方式训练模型。基准数据集的实验结果表明，各种知识的注入可改善强大的神经网络基线。我们方法的另一个好处是，该图本身自然是决策背后的理性。
We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner. Experimental results on a benchmark dataset show that the injection of various types of knowledge improves a strong neural network baseline. An additional benefit of our approach is that the graph itself naturally serves as a rational behind the decision making.
