论文标题
具有属性感知环境流的程序阅读理解
Procedural Reading Comprehension with Attribute-Aware Context Flow
论文作者
论文摘要
程序文本经常描述发生在实体（例如光，食物）上的过程（例如，光合作用和烹饪）。在本文中，我们通过将文本转换为一般形式主义来引入一种用于程序阅读理解的算法，该算法将过程表示为实体属性（例如，位置，温度）的过渡顺序。利用预训练的语言模型，我们的模型通过实体属性及其过渡的联合预测获得了文本的实体感知和属性意识表示。我们的模型动态地获取了程序文本的上下文编码，该过程利用了有关以前和当前状态的信息，以预测某个属性的过渡，该属性可以识别为文本的跨度或从预定的一组类中。此外，我们的模型在两个程序阅读理解数据集上实现了最新的最新技术，即propara和npn-cooking
Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature). Leveraging pre-trained language models, our model obtains entity-aware and attribute-aware representations of the text by joint prediction of entity attributes and their transitions. Our model dynamically obtains contextual encodings of the procedural text exploiting information that is encoded about previous and current states to predict the transition of a certain attribute which can be identified as a span of text or from a pre-defined set of classes. Moreover, our model achieves state of the art results on two procedural reading comprehension datasets, namely ProPara and npn-cooking
