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In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into state-of-the-art neural models for procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models. 

Recent research has shown that integrating domain knowledge into deep learning architectures is effective – it helps reduce the amount of required data, improves the accuracy of the models’ decisions, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models. 

Tracking entities throughout a procedure de- scribed in a text is challenging due to the dy- namic nature of the world described in the pro- cess. Firstly, we propose to formulate this task as a question answering problem. This en- ables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text un- derstanding. Secondly, since the transformer- based language models cannot encode the flow of events by themselves, we propose a Time- Stamped Language Model (TSLM model) to encode event information in LMs architec- ture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state- of-the-art results with a 3.1% increase in F1 score. Moreover, our model yields better re- sults on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.