Recent studies show that sentence-level extractive QA, i.e., based on Answer
Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA)
models, which generate answers using the top-k answer sentences ranked by AS2
models (a la retrieval-augmented generation style). In this paper, we propose a
novel training paradigm for GenQA using supervision from automatic QA
evaluation models (GAVA). Specifically, we propose three strategies to transfer
knowledge from these QA evaluation models to a GenQA model: (i) augmenting
training data with answers generated by the GenQA model and labelled by GAVA
(either statically, before training, or (ii) dynamically, at every training
epoch); and (iii) using the GAVA score for weighting the generator loss during
the learning of the GenQA model. We evaluate our proposed methods on two
academic and one industrial dataset, obtaining a significant improvement in
answering accuracy over the previous state of the art.

本研究通过提出一种新的 GenQA 训练范式，利用自动 QA 评估模型（GAVA）中的监督进行知识转移，比以往的基于 AS2 模型的抽取式 QA 方法有更高的准确率，方法包括：用由 GAVA 标记的 GenQA 模型生成的答案增加训练数据，使用 GAVA 分数加权生成器损失以及动态标记生成的答案。