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        <title>CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text</title>
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        <namePart type="given">Abhilash</namePart>
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        <namePart type="given">Manav</namePart>
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            <namePart type="given">Kalika</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.589</identifier>
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%0 Conference Proceedings
%T CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text
%A Nandy, Abhilash
%A Kapadnis, Manav
%A Goyal, Pawan
%A Ganguly, Niloy
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F nandy-etal-2023-clmsm
%X 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.
%R 10.18653/v1/2023.findings-emnlp.589
%U https://aclanthology.org/2023.findings-emnlp.589
%U https://doi.org/10.18653/v1/2023.findings-emnlp.589
%P 8793-8806

Markdown (Informal)

[CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text](https://aclanthology.org/2023.findings-emnlp.589) (Nandy et al., Findings 2023)

• CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text (Nandy et al., Findings 2023)

ACL

• Abhilash Nandy, Manav Kapadnis, Pawan Goyal, and Niloy Ganguly. 2023. CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8793–8806, Singapore. Association for Computational Linguistics.