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.
翻译:常识推理系统应该能够对各种推理案例进行概括化分析,然而,大多数最先进的方法依赖于昂贵的数据说明,而且不学习如何执行一般语义推理,就过于适合特定基准,而不学习如何执行一般语义推理。为克服这些缺陷,零弹射质量评估系统通过将常识知识图(KG)转化为综合质量评估模型培训的样本,将前景看成一个强有力的学习计划。考虑到不同常识知识图(KG)的种类越来越多,本文件的目的是将零弹射转移学习假想扩展至多种源环境,使不同的KGs能够被用于相互交错的。为了实现这一目标,我们提议通过开发知识组合的模块变体,作为新的零弹射常识推理框架,减少知识因不同知识来源之间的干扰而丧失的知识。五个常识推理基准展示了我们框架的功效,用多个KGs改进了业绩。