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- W4320169235 abstract "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." @default.
- W4320169235 created "2023-02-13" @default.
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- W4320169235 date "2022-06-08" @default.
- W4320169235 modified "2023-10-16" @default.
- W4320169235 title "Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning" @default.
- W4320169235 doi "https://doi.org/10.48550/arxiv.2206.03715" @default.
- W4320169235 hasPublicationYear "2022" @default.
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