In order to facilitate natural language understanding, the key is to engage
commonsense or background knowledge. However, how to engage commonsense
effectively in question answering systems is still under exploration in both
research academia and industry. In this paper, we propose a novel
question-answering method by integrating multiple knowledge sources, i.e.
ConceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance.
More concretely, we first introduce a novel graph-based iterative knowledge
retrieval module, which iteratively retrieves concepts and entities related to
the given question and its choices from multiple knowledge sources. Afterward,
we use a pre-trained language model to encode the question, retrieved knowledge
and choices, and propose an answer choice-aware attention mechanism to fuse all
hidden representations of the previous modules. Finally, the linear classifier
for specific tasks is used to predict the answer. Experimental results on the
CommonsenseQA dataset show that our method significantly outperforms other
competitive methods and achieves the new state-of-the-art. In addition, further
ablation studies demonstrate the effectiveness of our graph-based iterative
knowledge retrieval module and the answer choice-aware attention module in
retrieving and synthesizing background knowledge from multiple knowledge
sources.

该研究提出了一种将多种知识源集成起来的问题回答方法，着重利用基于图的迭代知识检索模块和答案感知的注意机制，从多个知识源中检索和综合背景知识，并在 CommonsenseQA 数据集上实现了最优的表现。