Neural Machine Translation for Conditional Generation of Novel Procedures

Abstract

Procedural knowledge is generally dispersed across many experts within or across organizations which might lead to inefficiencies and redundancy. Historically, computers have been well suited to store procedural knowledge but they have lacked the capability to produce natural language text. Nonetheless, recent advances in machine learning permit a higher linguistic coherence which benefits applications with longer text outputs such as procedures. This work closes the gap between human experts and computers by proposing a framework for automatic, computer generation of procedures based on neural machine translation and the BART model. Furthermore, we define two benchmark problems for procedure generation and establish a set of evaluation metrics that can be used as a reference in further work. We demonstrate the potential of this solution on the task of generating cooking recipes based on available ingredients. The evaluation results on the Recipe1M dataset showcase the method's superiority over other, fairly novel, neural architectures.