  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.
