Commonsense question-answering (QA) methods combine the power of pre-trained
Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A
typical approach collects nodes relevant to the QA pair from a KG to form a
Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs).
This faces two major challenges: (i) it is difficult to capture all the
information from the QA in the WG, and (ii) the WG contains some irrelevant
nodes from the KG. To address these, we propose GrapeQA with two simple
improvements on the WG: (i) Prominent Entities for Graph Augmentation
identifies relevant text chunks from the QA pair and augments the WG with
corresponding latent representations from the LM, and (ii) Context-Aware Node
Pruning removes nodes that are less relevant to the QA pair. We evaluate our
results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows
consistent improvements over its LM + KG predecessor (QA-GNN in particular) and
large improvements on OpenBookQA.

GrapeQA 是一种基于语言模型的 Commonsense QA 方法，采用知识图谱以及图神经网络进行推理，提出了 Prominent Entities for Graph Augmentation 和 Context-Aware Node Pruning 两个简单的改进方法来解决捕捉 QA 信息和去除无关节点的问题，并在 OpenBookQA、CommonsenseQA 和 MedQA-USMLE 上展示出比 QA-GNN 更出色的结果。