Combining Data Generation and Active Learning for Low-Resource Question Answering
This work addresses the challenge of building question answering systems in new, specialized domains with limited labeled data, which is incremental as it integrates existing techniques.
The authors tackled the problem of low-resource question answering by combining data augmentation via question-answer generation with Active Learning, resulting in improved performance in domain-specific settings with reduced human annotation effort.
Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low-resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain. We also investigate Active Learning for question answering in different stages, overall reducing the annotation effort of humans. For this purpose, we consider target domains in realistic settings, with an extremely low amount of annotated samples but with many unlabeled documents, which we assume can be obtained with little effort. Additionally, we assume a sufficient amount of labeled data from the source domain being available. We perform extensive experiments to find the best setup for incorporating domain experts. Our findings show that our novel approach, where humans are incorporated in a data generation approach, boosts performance in the low-resource, domain-specific setting, allowing for low-labeling-effort question answering systems in new, specialized domains. They further demonstrate how human annotation affects the performance of QA depending on the stage it is performed.