  An effective paradigm for building Automated Question Answering systems is
the re-use of previously answered questions, e.g., for FAQs or forum
applications. Given a database (DB) of question/answer (q/a) pairs, it is
possible to answer a target question by scanning the DB for similar questions.
In this paper, we scale this approach to open domain, making it competitive
with other standard methods, e.g., unstructured document or graph based. For
this purpose, we (i) build a large scale DB of 6.3M q/a pairs, using public
questions, (ii) design a new system based on neural IR and a q/a pair reranker,
and (iii) construct training and test data to perform comparative experiments
with our models. We demonstrate that Transformer-based models using (q,a) pairs
outperform models only based on question representation, for both neural search
and reranking. Additionally, we show that our DB-based approach is competitive
with Web-based methods, i.e., a QA system built on top the BING search engine,
demonstrating the challenge of finding relevant information. Finally, we make
our data and models available for future research.
