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Paraphrasing is rooted in semantics. We show the effectiveness oftransformers (Vaswani et al. 2017) for paraphrase generation and furtherimprovements by incorporating PropBank labels via a multi-encoder. Evaluatingon MSCOCO and WikiAnswers, we find that transformers are fast and effective,and that semantic augmentation for both transformers and LSTMs leads to sizable2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisinglylarge gains on human evaluations compared to previous models. Nevertheless,manual inspection of generated paraphrases reveals ample room for improvement:even our best model produces human-acceptable paraphrases for only 28% ofcaptions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularlyon sentences from Wikipedia. Overall, these results point to the potential forincorporating semantics in the task while highlighting the need for strongerevaluation.

A task in a suit and a tie: paraphrase generation with semantic augmentation