  An essential task of most Question Answering (QA) systems is to re-rank the
set of answer candidates, i.e., Answer Sentence Selection (A2S). These
candidates are typically sentences either extracted from one or more documents
preserving their natural order or retrieved by a search engine. Most
state-of-the-art approaches to the task use huge neural models, such as BERT,
or complex attentive architectures. In this paper, we argue that by exploiting
the intrinsic structure of the original rank together with an effective
word-relatedness encoder, we can achieve competitive results with respect to
the state of the art while retaining high efficiency. Our model takes 9.5
seconds to train on the WikiQA dataset, i.e., very fast in comparison with the
$\sim 18$ minutes required by a standard BERT-base fine-tuning.
