Answering Spatial Commonsense Questions by Learning Domain-Invariant Generalization Knowledge

Existing spatial commonsense reading comprehension (SCRC) systems struggle to answer questions from unknown domains or any out-of-domain distributions, which prevents them from being deployed in real applications. Unsupervised domain adaptation (UDA) in QA has emerged as a major approach to address this challenge. However, existing methods mainly rely on generating synthetic data and pseudo-labeling target domains, which not only consumes extra computational resources but also places high demands on noise filtering of the generated data. To tackle these problems, we propose a UDA framework, called LEGRN-DIG, for spatial commonsense question answering using unlabeled target domain data. This framework avoids the use of labeled or pseudo-labeled target instances and noisy synthetic data. We apply domain-invariant generalization learning to integrate features of the target domain into the source domain and still use the source domain for supervised training. Extensive experiments are conducted to illustrate the effectiveness and robustness of our model.