Abstract
Paraphrase generation can be regarded as
monolingual translation. Unlike bilingual
machine translation, paraphrase generation
rewrites only a limited portion of an input
sentence. Hence, previous methods based on
machine translation often perform conservatively to fail to make necessary rewrites. To
solve this problem, we propose a neural model
for paraphrase generation that first identifies
words in the source sentence that should be
paraphrased. Then, these words are paraphrased by the negative lexically constrained
decoding that avoids outputting these words as
they are. Experiments on text simplification
and formality transfer show that our model improves the quality of paraphrasing by making
necessary rewrites to an input sentence