Abstract
This paper is concerned with the task of
multi-hop open-domain Question Answering
(QA). This task is particularly challenging
since it requires the simultaneous performance
of textual reasoning and efficient searching.
We present a method for retrieving multiple
supporting paragraphs, nested amidst a large
knowledge base, which contain the necessary
evidence to answer a given question. Our
method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The
retrieval is performed by considering contextualized sentence-level representations of the
paragraphs in the knowledge source. Our
method achieves state-of-the-art performance
over two well-known datasets, SQuAD-Open
and HotpotQA, which serve as our single- and
multi-hop open-domain QA benchmarks, respectively