Recent advancements in transformer-based models have greatly improved the
ability of Question Answering (QA) systems to provide correct answers; in
particular, answer sentence selection (AS2) models, core components of
retrieval-based systems, have achieved impressive results. While generally
effective, these models fail to provide a satisfying answer when all retrieved
candidates are of poor quality, even if they contain correct information. In
AS2, models are trained to select the best answer sentence among a set of
candidates retrieved for a given question. In this work, we propose to generate
answers from a set of AS2 top candidates. Rather than selecting the best
candidate, we train a sequence to sequence transformer model to generate an
answer from a candidate set. Our tests on three English AS2 datasets show
improvement up to 32 absolute points in accuracy over the state of the art.

本文通过提出一种基于序列到序列变换器模型的答案生成方法，从 AS2 候选中生成答案，并在三个英文 AS2 数据集上进行测试，结果显示本方法的准确率在现有技术水平上提高了 32 个绝对值的水平。