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arXiv · 2310.14326

CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text

Abhilash Nandy·Manav Nitin Kapadnis·Pawan Goyal·Niloy Ganguly
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Abstract

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.

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Abhilash Nandy, Manav Nitin Kapadnis, Pawan Goyal, Niloy Ganguly. 2023-10-22. CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text. https://arxiv.org/abs/2310.14326

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