  The conventional paradigm in neural question answering (QA) for narrative
content is limited to a two-stage process: first, relevant text passages are
retrieved and, subsequently, a neural network for machine comprehension
extracts the likeliest answer. However, both stages are largely isolated in the
status quo and, hence, information from the two phases is never properly fused.
In contrast, this work proposes RankQA: RankQA extends the conventional
two-stage process in neural QA with a third stage that performs an additional
answer re-ranking. The re-ranking leverages different features that are
directly extracted from the QA pipeline, i.e., a combination of retrieval and
comprehension features. While our intentionally simple design allows for an
efficient, data-sparse estimation, it nevertheless outperforms more complex QA
systems by a significant margin: in fact, RankQA achieves state-of-the-art
performance on 3 out of 4 benchmark datasets. Furthermore, its performance is
especially superior in settings where the size of the corpus is dynamic. Here
the answer re-ranking provides an effective remedy against the underlying
noise-information trade-off due to a variable corpus size. As a consequence,
RankQA represents a novel, powerful, and thus challenging baseline for future
research in content-based QA.
