Probabilistic word embeddings have shown effectiveness in capturing notions
of generality and entailment, but there is very little work on doing the
analogous type of investigation for sentences. In this paper we define
probabilistic models that produce distributions for sentences. Our
best-performing model treats each word as a linear transformation operator
applied to a multivariate Gaussian distribution. We train our models on
paraphrases and demonstrate that they naturally capture sentence specificity.
While our proposed model achieves the best performance overall, we also show
that specificity is represented by simpler architectures via the norm of the
sentence vectors. Qualitative analysis shows that our probabilistic model
captures sentential entailment and provides ways to analyze the specificity and
preciseness of individual words.

本文定义了能够为句子产生分布的概率模型，最佳表现的模型将每个单词视为线性转换算子应用于多变量高斯分布，并通过从定义的模型中进行训练，演示了实现句子特定性的自然方式。虽然我们的模型是整体最佳表现的，但通过句子向量的范数表示来表示句子的特异性的简单架构也能够表现句子的特异性。质性分析表明，我们的概率模型捕获了句子涵盖并提供了分析单词特异性和精确性的方法。