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Title: Synthetic Question Value Estimation for Domain Adaptation of Question Answering
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.  more » « less
Award ID(s):
1942980
NSF-PAR ID:
10334241
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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