Non-extractive commonsense QA remains a challenging AI task, as it requires
systems to reason about, synthesize, and gather disparate pieces of
information, in order to generate responses to queries. Recent approaches on
such tasks show increased performance, only when models are either pre-trained
with additional information or when domain-specific heuristics are used,
without any special consideration regarding the knowledge resource type. In
this paper, we perform a survey of recent commonsense QA methods and we provide
a systematic analysis of popular knowledge resources and knowledge-integration
methods, across benchmarks from multiple commonsense datasets. Our results and
analysis show that attention-based injection seems to be a preferable choice
for knowledge integration and that the degree of domain overlap, between
knowledge bases and datasets, plays a crucial role in determining model
success.

本文针对非萃取式常识问答（QA）这一具有挑战性的 AI 任务进行了探讨，对最近的常识 QA 方法、流行的知识资源和知识融合方法进行了系统分析，并研究了多个常识数据集上的基准测试。结果表明，注重知识融合的注意力注入方式似乎是一种较好的选择，并且知识库与数据集之间的领域重叠度对于确定模型的成功程度具有至关重要的作用。