In this paper, we build on this recent innovation D2KE (Wu et al., 2018a), and present the Word Mover's Embedding (WME), an unsupervised generic framework that learns continuous vector representations for text of variable lengths such as a sentence, paragraph, or document.
we propose a new approach to first construct a positive-definite Word Mover's Kernel via an infinite-dimensional feature map given by the Word Mover's distance (WMD) to random documents from a given distribution.