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 human-written sentences, finding
clear differences in length, the amount of repetition, and the use of rare
words.

本研究使用神经机器翻译通过回译双语句子对生成句子的解释，并对语料对进行了筛选处理，在此基础上建立通用的释义句子表示模型，结果显示该模型对于很多语言领域其正确率与手动编写的英语释义句子相当。