Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from humanwritten sentences, finding clear differences in length, the amount of repetition, and the use of rare words. 1 1 Generated paraphrases and code are available at http: //ttic.uchicago.edu/ wieting. R: We understand that has already commenced, but there is a long way to go. T: This situation has already commenced, but much still needs to be done. R: The restaurant is closed on Sundays. No breakfast is available on Sunday mornings. T: The restaurant stays closed Sundays so no breakfast is served these days. R: Improved central bank policy is another huge factor. T: Another crucial factor is