Recent studies show that Question Answering (QA) based on Answer Sentence
Selection (AS2) can be improved by generating an improved answer from the top-k
ranked answer sentences (termed GenQA). This allows for synthesizing the
information from multiple candidates into a concise, natural-sounding answer.
However, creating large-scale supervised training data for GenQA models is very
challenging. In this paper, we propose to train a GenQA model by transferring
knowledge from a trained AS2 model, to overcome the aforementioned issue.
First, we use an AS2 model to produce a ranking over answer candidates for a
set of questions. Then, we use the top ranked candidate as the generation
target, and the next k top ranked candidates as context for training a GenQA
model. We also propose to use the AS2 model prediction scores for loss
weighting and score-conditioned input/output shaping, to aid the knowledge
transfer. Our evaluation on three public and one large industrial datasets
demonstrates the superiority of our approach over the AS2 baseline, and GenQA
trained using supervised data.

本文提出一种通过从 AS2 模型中传递知识来训练 GenQA 模型的方法，该模型将候选答案进行排序，使用排名最高的答案作为生成目标，并使用前 k 个排名最高的答案作为上下文进行训练。模型还利用 AS2 模型的预测得分进行损失权重和条件输入 / 输出塑形，以协助知识传递，并在三个公共数据集和一个大型工业数据集上的评估中证明了该方法优于 AS2 基线和使用监督数据训练的 GenQA。