  In this paper, we introduce a novel natural language generation task, termed
as text morphing, which targets at generating the intermediate sentences that
are fluency and smooth with the two input sentences. We propose the Morphing
Networks consisting of the editing vector generation networks and the sentence
editing networks which are trained jointly. Specifically, the editing vectors
are generated with a recurrent neural networks model from the lexical gap
between the source sentence and the target sentence. Then the sentence editing
networks iteratively generate new sentences with the current editing vector and
the sentence generated in the previous step. We conduct experiments with 10
million text morphing sequences which are extracted from the Yelp review
dataset. Experiment results show that the proposed method outperforms baselines
on the text morphing task. We also discuss directions and opportunities for
future research of text morphing.
