Abstract:As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases.
We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two at… Show more
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