The task of zero-shot commonsense question answering evaluates models on
their capacity to reason about general scenarios beyond those presented in
specific datasets. Existing approaches for tackling this task leverage external
knowledge from CommonSense Knowledge Bases (CSKBs) by pretraining the model on
synthetic QA pairs constructed from CSKBs. In these approaches, negative
examples (distractors) are formulated by randomly sampling from CSKBs using
fairly primitive keyword constraints. However, two bottlenecks limit these
approaches: the inherent incompleteness of CSKBs limits the semantic coverage
of synthetic QA pairs, and the lack of human annotations makes the sampled
negative examples potentially uninformative and contradictory. To tackle these
limitations above, we propose Conceptualization-Augmented Reasoner (CAR), a
zero-shot commonsense question-answering framework that fully leverages the
power of conceptualization. Specifically, CAR abstracts a commonsense knowledge
triple to many higher-level instances, which increases the coverage of CSKB and
expands the ground-truth answer space, reducing the likelihood of selecting
false-negative distractors. Extensive experiments demonstrate that CAR more
robustly generalizes to answering questions about zero-shot commonsense
scenarios than existing methods, including large language models, such as
GPT3.5 and ChatGPT. Our codes, data, and model checkpoints are available at
this https URL

提出了一种 Zero-shot Commonsense Question Answering 任务的新方法（CAR），通过概念抽象化和知识三元组，大大增加了 CommonSense Knowledge Bases 的语义覆盖范围，使负样本的选择更有效，从而比 GPT3.5 等方法在处理零样本常识情境问题时表现更出色。