Learning to Perturb Word Embeddings for Out-of-distribution QA

QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data. Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task. To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. We validate the performance of the QA models trained with our word embedding perturbation on a single source dataset, on five different target domains. The results show that our method significantly outperforms the baseline DA methods. Notably, the model trained with ours outperforms the model trained with more than 240K artificially generated QA pairs. Q: In what year was the Theodore m. Hesburgh library at Notre Dame finished? C: (…) the main building is the 14 -story Theodore m. Hesburgh library, completed in 1963, (…) this mural is popularly known as "touchdown jesus" because of its proximity … Q: In what year was the Theodore m. Hesburgh library at Notre Dame finished? Q: each last year was the Theodore m. Vanroth library at Notre Dame finished. C: (…) the first building is the 14 -story Theodore p von Hesburgh library, completed in 1963 ; (…) this mural is popularly known as our confession jesus christ because all its … C: (…) the main building is the 14 -story Theodore m. Hesburgh library, completed in 1963, (…) this mural is popularly known as "touchdown jesus" because of its proximity …