  We present a neural model for paraphrasing and train it to generate
delexicalized sentences. We achieve this by creating training data in which
each input is paired with a number of reference paraphrases. These sets of
reference paraphrases represent a weak type of semantic equivalence based on
annotated slots and intents. To understand semantics from different types of
slots, other than anonymizing slots, we apply convolutional neural networks
(CNN) prior to pooling on slot values and use pointers to locate slots in the
output. We show empirically that the generated paraphrases are of high quality,
leading to an additional 1.29% exact match on live utterances. We also show
that natural language understanding (NLU) tasks, such as intent classification
and named entity recognition, can benefit from data augmentation using
automatically generated paraphrases.
