论文标题
知识辅助开放域问题回答
Knowledge-Aided Open-Domain Question Answering
论文作者
论文摘要
开放域问题答案（QA）旨在从大量文档中找到一个问题的答案。尽管许多单案机器理解的模型已经达到了强大的性能，但仍然有很大的空间可以改善开放式质量警察质量固定系统，因为文档的检索和答案的重读仍然不满意。检索组件可能无法正确评分包含正确答案的黄金文档，并且提取的正确答案可能会在其他候选人的答案中由重新加工组件进行错误排名。原因之一是从独立原则中得出的，其中每个候选文件（或答案）是独立评分的，而无需考虑其与其他文件（或答案）的关系。在这项工作中，我们提出了一种知识辅助的开放域质量主（KAQA）方法，该方法的目标是通过考虑问题与文档之间的关系（称为询问文档图）以及候选文档之间的关系（称为文档文件图表图），以改善相关文档检索和候选答案重新管理。这些图是使用外部知识资源的知识三元组构建的。在文件检索期间，通过考虑其与问题和其他文件的关系来评估候选文件。在回答重新播放期间，不仅使用其自己的上下文，而且还使用其他文件的线索来重新候选答案。实验结果表明，我们提出的方法改善了文档的检索和回答，从而提高了开放域问答的整体性能。
Open-domain question answering (QA) aims to find the answer to a question from a large collection of documents.Though many models for single-document machine comprehension have achieved strong performance, there is still much room for improving open-domain QA systems since document retrieval and answer reranking are still unsatisfactory. Golden documents that contain the correct answers may not be correctly scored by the retrieval component, and the correct answers that have been extracted may be wrongly ranked after other candidate answers by the reranking component. One of the reasons is derived from the independent principle in which each candidate document (or answer) is scored independently without considering its relationship to other documents (or answers). In this work, we propose a knowledge-aided open-domain QA (KAQA) method which targets at improving relevant document retrieval and candidate answer reranking by considering the relationship between a question and the documents (termed as question-document graph), and the relationship between candidate documents (termed as document-document graph). The graphs are built using knowledge triples from external knowledge resources. During document retrieval, a candidate document is scored by considering its relationship to the question and other documents. During answer reranking, a candidate answer is reranked using not only its own context but also the clues from other documents. The experimental results show that our proposed method improves document retrieval and answer reranking, and thereby enhances the overall performance of open-domain question answering.
