论文标题
域的综合问题价值估计域的适应问题答案
Synthetic Question Value Estimation for Domain Adaptation of Question Answering
论文作者
论文摘要
在目标域上与问题发生器（QG）合成的QA对已成为一种流行的方法，以适应问题答案（QA）模型。由于合成问题通常在实践中嘈杂，因此现有的工作将验证的QA（或QG）模型的分数作为选择高质量问题的标准。但是，这些分数并不能直接实现提高目标域上质量检查性能的最终目标。在本文中，我们介绍了一个新颖的概念，即训练问题价值估计器（QVE），该想法直接估计了合成问题在改善目标域质量统计局性能方面的有用性。通过进行全面的实验，我们表明，与现有技术相比，QVE选择的综合问题可以帮助实现更好的目标域QA性能。我们还表明，通过使用此类问题，仅在目标域上的人类注释的15％左右，我们就可以实现与完全监督的基线相当的性能。
Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often noisy in practice, existing work adapts scores from a pretrained QA (or QG) model as criteria to select high-quality questions. However, these scores do not directly serve the ultimate goal of improving QA performance on the target domain. In this paper, we introduce a novel idea of training a question value estimator (QVE) that directly estimates the usefulness of synthetic questions for improving the target-domain QA performance. By conducting comprehensive experiments, we show that the synthetic questions selected by QVE can help achieve better target-domain QA performance, in comparison with existing techniques. We additionally show that by using such questions and only around 15% of the human annotations on the target domain, we can achieve comparable performance to the fully-supervised baselines.
