  Procedural text describes dynamic state changes during a step-by-step natural
process (e.g., photosynthesis). In this work, we focus on the task of
procedural text understanding, which aims to comprehend such documents and
track entities' states and locations during a process. Although recent
approaches have achieved substantial progress, their results are far behind
human performance. Two challenges, the difficulty of commonsense reasoning and
data insufficiency, still remain unsolved, which require the incorporation of
external knowledge bases. Previous works on external knowledge injection
usually rely on noisy web mining tools and heuristic rules with limited
applicable scenarios. In this paper, we propose a novel KnOwledge-Aware
proceduraL text understAnding (KOALA) model, which effectively leverages
multiple forms of external knowledge in this task. 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.
