Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences. Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific. We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. Our proposed approach differs from past work on semantic sentence encoding in two ways. First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model's posterior to predict sentence embeddings for monolingual data at test time. Second, we use high-capacity transformers as both data generating distributions and inference networks -- contrasting with most past work on sentence embeddings. In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations. Further, we demonstrate that our approach yields the largest gains on more difficult subsets of these evaluations where simple word overlap is not a good indicator of similarity.

本文提出了一种基于变分概率框架的深度潜变量模型，使用双语数据，利用拥有共性的语义信息与特性进行训练，使模型可以将并行语句进行源分离，得到隐含的语义向量，进而对单语数据进行预测。实验证明，该方法在无监督语义相似性评估方面的表现明显优于现有方法，并且还能在不适合使用简单词语重叠作为相似性指标的更难评估子集中发挥最大的作用。

双语生成式Transformer用于语义句子嵌入