  Data augmentation seeks to manipulate the available data for training to
improve the generalization ability of models. We investigate two data
augmentation proxies, permutation and flipping, for neural dialog response
selection task on various models over multiple datasets, including both Chinese
and English languages. Different from standard data augmentation techniques,
our method combines the original and synthesized data for prediction. Empirical
results show that our approach can gain 1 to 3 recall-at-1 points over baseline
models in both full-scale and small-scale settings.
