A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection

Understand

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.