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In order to facilitate natural language understanding, the key is to engagecommonsense or background knowledge. However, how to engage commonsenseeffectively in question answering systems is still under exploration in bothresearch academia and industry. In this paper, we propose a novelquestion-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 knowledgeretrieval module, which iteratively retrieves concepts and entities related tothe given question and its choices from multiple knowledge sources. Afterward,we use a pre-trained language model to encode the question, retrieved knowledgeand choices, and propose an answer choice-aware attention mechanism to fuse allhidden representations of the previous modules. Finally, the linear classifierfor specific tasks is used to predict the answer. Experimental results on theCommonsenseQA dataset show that our method significantly outperforms othercompetitive methods and achieves the new state-of-the-art. In addition, furtherablation studies demonstrate the effectiveness of our graph-based iterativeknowledge retrieval module and the answer choice-aware attention module inretrieving and synthesizing background knowledge from multiple knowledgesources.

Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources