We focus on the task of procedural text understanding, which aims to track entities' states and locations during a natural process. Although recent approaches have achieved substantial progress, they are far behind human performance. Two challenges, difficulty of commonsense reasoning and data insufficiency, still remain unsolved. In this paper, we propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which leverages external knowledge sources to solve these issues. Specifically, we retrieve informative knowledge triples from ConceptNet and perform knowledge-aware reasoning while tracking the entities. Besides, we employ a multi-stage training schema which fine-tunes the BERT model over unlabeled data collected from Wikipedia before further fine-tuning it on the final model. Experimental results on two procedural text datasets, ProPara and Recipes, verify the effectiveness of the proposed methods, in which our model achieves state-of-the-art performance in comparison to various baselines.

研究了手续性文本理解的挑战，发现外部知识注入和数据不足的问题，提出了一个多阶段训练的知识感知KOALA模型，最终在ProPara和Recipes数据集上达到了最先进的性能表现。

多阶段训练的知识感知程序文本理解