  We present a novel technique for zero-shot paraphrase generation. The key
contribution is an end-to-end multilingual paraphrasing model that is trained
using translated parallel corpora to generate paraphrases into "meaning spaces"
-- replacing the final softmax layer with word embeddings. This architectural
modification, plus a training procedure that incorporates an autoencoding
objective, enables effective parameter sharing across languages for more fluent
monolingual rewriting, and facilitates fluency and diversity in generation. Our
continuous-output paraphrase generation models outperform zero-shot
paraphrasing baselines when evaluated on two languages using a battery of
computational metrics as well as in human assessment.
