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        <title>Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models</title>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement. To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.</abstract>
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%0 Conference Proceedings
%T Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models
%A Klein, Tassilo
%A Nabi, Moin
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F klein-nabi-2021-towards
%X Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement. To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.
%R 10.18653/v1/2021.emnlp-main.688
%U https://aclanthology.org/2021.emnlp-main.688
%U https://doi.org/10.18653/v1/2021.emnlp-main.688
%P 8737-8743

Markdown (Informal)

[Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models](https://aclanthology.org/2021.emnlp-main.688) (Klein & Nabi, EMNLP 2021)

• Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models (Klein & Nabi, EMNLP 2021)

ACL

• Tassilo Klein and Moin Nabi. 2021. Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8737–8743, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.