  In this paper, we propose CLMSM, a domain-specific, continual pre-training
framework, that learns from a large set of procedural recipes. CLMSM uses a
Multi-Task Learning Framework to optimize two objectives - a) Contrastive
Learning using hard triplets to learn fine-grained differences across entities
in the procedures, and b) a novel Mask-Step Modelling objective to learn
step-wise context of a procedure. We test the performance of CLMSM on the
downstream tasks of tracking entities and aligning actions between two
procedures on three datasets, one of which is an open-domain dataset not
conforming with the pre-training dataset. We show that CLMSM not only
outperforms baselines on recipes (in-domain) but is also able to generalize to
open-domain procedural NLP tasks.
