  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.
