论文标题
知识融合和语义知识排名开放域问题回答
Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering
论文作者
论文摘要
开放域问题答案需要系统来检索外部知识并通过在多个句子上构图知识来执行多跳的推理。在最近引入的开放式域问题回答挑战数据集，QASC和OpenBookQA中，我们需要进行事实检索并构成事实以正确回答问题。在我们的工作中，我们学习了一种语义知识排名模型，以通过基于Lucene的信息检索系统重新排列知识。我们进一步提出了一个“知识融合模型”，该模型利用基于BERT的语言模型的知识，并以外部检索知识并提高对基于BERT的语言模型的知识理解。在OpenBookQA和QASC数据集上，具有语义重新排名知识的知识融合模型优于以前的尝试。
Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recently introduced open domain question answering challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts and compose facts to correctly answer questions. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We further propose a "knowledge fusion model" which leverages knowledge in BERT-based language models with externally retrieved knowledge and improves the knowledge understanding of the BERT-based language models. On both OpenBookQA and QASC datasets, the knowledge fusion model with semantically re-ranked knowledge outperforms previous attempts.
