Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision. In this paper, we formalize the implicit similarity function induced by this approach, and show that it is susceptible to non-paraphrase pairs sharing a single ambiguous translation. Based on these insights, we design an alternative similarity metric that mitigates this issue by requiring the entire translation distribution to match, and implement a relaxation of it through the Information Bottleneck method. Our approach incorporates an adversarial term into MT training in order to learn representations that encode as much information about the reference translation as possible, while keeping as little information about the input as possible. Paraphrases can be generated by decoding back to the source from this representation, without having to generate pivot translations. In addition to being more principled and efficient than round-trip MT, our approach offers an adjustable parameter to control the fidelity-diversity trade-off, and obtains better results in our experiments.

翻译：往返机器翻译（Round-trip MT）是释义生成中常用的一种方法，它利用现成的平行语料进行监督。本文首先形式化了该方法所隐含的相似性函数，并指出该函数易受共享单一歧义翻译的非释义句对影响。基于这些发现，我们设计了一种替代性相似度度量，通过要求整个翻译分布相匹配来缓解此问题，并通过信息瓶颈方法实现了该度量的松弛化处理。该方法在机器翻译训练中引入对抗项，使学习到的表征在尽可能保留参考翻译信息的同时，最小化对输入信息的编码。通过从该表征解码回源语言即可生成释义，无需先生成枢轴翻译。相较于往返机器翻译，我们的方法不仅更具规范性和高效性，还提供了可调节参数以控制保真度-多样性权衡，并在实验中取得了更优结果。