Unsupervised Domain Adaptation on Reading Comprehension

Understand

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks.

• However, the generalization capability of these models across different domains remains unclear.
• To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples.
• We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset.