We extend the work of Wieting et al. (2017), back-translating a large parallel corpus to produce a dataset of more than 51 million English-English sentential paraphrase pairs in a dataset we call ParaNMT-50M. We find this corpus to be cover many domains and styles of text, in addition to being rich in paraphrases with different sentence structure, and we release it to the community. To show its utility, we use it to train paraphrastic sentence embeddings using only minor changes to the framework of Wieting et al. (2016b). The resulting embeddings outperform all supervised systems on every SemEval semantic textual similarity (STS) competition, and are a significant improvement in capturing paraphrastic similarity over all other sentence embeddings We also show that our embeddings perform competitively on general sentence embedding tasks. We release the corpus, pretrained sentence embeddings, and code to generate them. We believe the corpus can be a valuable resource for automatic paraphrase generation and can provide a rich source of semantic information to improve downstream natural language understanding tasks.

使用神经机器翻译将大型平行语料库的非英语部分翻译为英语，生成超过五千万个英语句子的同义句数据集PARANMT-50M，该数据集可用于同义句生成，提供丰富的语义知识以改善下游自然语言理解任务。我们使用PARANMT-50M训练了同义句嵌入，能在所有SemEval基于语义相似度的比赛中胜过所有监督系统，并展示了它在同义句生成中的应用。

ParaNMT-50M：用数百万机器翻译推进释义句嵌入的极限