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Commonsense reasoning systems should be able to generalize to diversereasoning cases. However, most state-of-the-art approaches depend on expensivedata annotations and overfit to a specific benchmark without learning how toperform general semantic reasoning. To overcome these drawbacks, zero-shot QAsystems have shown promise as a robust learning scheme by transforming acommonsense knowledge graph (KG) into synthetic QA-form samples for modeltraining. Considering the increasing type of different commonsense KGs, thispaper aims to extend the zero-shot transfer learning scenario intomultiple-source settings, where different KGs can be utilized synergetically.Towards this goal, we propose to mitigate the loss of knowledge from theinterference among the different knowledge sources, by developing a modularvariant of the knowledge aggregation as a new zero-shot commonsense reasoningframework. Results on five commonsense reasoning benchmarks demonstrate theefficacy of our framework, improving the performance with multiple KGs.

Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning