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.
@article{arxiv.2110.13231,
title = {Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs},
author = {Monisha Jegadeesan and Sachin Kumar and John Wieting and Yulia Tsvetkov},
journal= {arXiv preprint arXiv:2110.13231},
year = {2021}
}