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

• URL: http://arxiv.org/abs/2109.05105v1
• Date: Fri, 10 Sep 2021 21:02:24 GMT
• Title: Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models
• Authors: Tassilo Klein and Moin Nabi
• Abstract summary: This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Challenge. 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.
• Score: 27.11678023496321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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