MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning

Anonymous

Abstract: Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models' commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel \textbf{M}ulti-m\textbf{O}dal \textbf{RE}trieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.

Paper Type: long

Research Area: Generation

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Data resources

Languages Studied: English