When to Read Documents or QA History: On Unified and Selective
  Open-domain QA
Kyungjae Lee Sang-eun Han Seung-won Hwang Moontae Lee
This paper studies the problem of open-domain question answering, with the
aim of answering a diverse range of questions leveraging knowledge resources.
Two types of sources, QA-pair and document corpora, have been actively
leveraged with the following complementary strength. The former is highly
precise when the paraphrase of given question $q$ was seen and answered during
training, often posed as a retrieval problem, while the latter generalizes
better for unseen questions. A natural follow-up is thus leveraging both
models, while a naive pipelining or integration approaches have failed to bring
additional gains over either model alone. Our distinction is interpreting the
problem as calibration, which estimates the confidence of predicted answers as
an indicator to decide when to use a document or QA-pair corpus. The
effectiveness of our method was validated on widely adopted benchmarks such as
Natural Questions and TriviaQA.