Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning. To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically. Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework. Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.
@article{arxiv.2206.03715,
title = {Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning},
author = {Yu Jin Kim and Beong-woo Kwak and Youngwook Kim and Reinald Kim Amplayo and Seung-won Hwang and Jinyoung Yeo},
journal= {arXiv preprint arXiv:2206.03715},
year = {2022}
}