Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product or cosine similarity, and enabling more expressive
parameterization of decision boundaries. This paper advocates for density-based
distributed embeddings and presents a method for learning representations in
the space of Gaussian distributions. We compare performance on various word
embedding benchmarks, investigate the ability of these embeddings to model
entailment and other asymmetric relationships, and explore novel properties of
the representation.

本文提倡使用基于密度的分布嵌入方法，并提出了一种在高斯分布空间中学习表示的方法，以更好地捕捉有关表示和其关系的不确定性，自然地表达不对称性，并使决策边界参数更有表达力。本文比较了各种单词嵌入基准测试的性能，调查了这些嵌入模型主导和其他不对称关系的能力，并探索了表示的新颖特性。