Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models

Tassilo Klein; Moin Nabi

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arXiv · 2109.05105

Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models

Tassilo Klein·Moin Nabi

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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.

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BibTeX RIS

Tassilo Klein, Moin Nabi. 2021-09-10. Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models. https://arxiv.org/abs/2109.05105

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