Word embedding with disentangling prior

Abstract

Described herein are system and method embodiments to improve word representation learning. Embodiments of a probabilistic prior may seamlessly integrate statistical disentanglement with word embedding. Different from previous deterministic methods, word embedding may be taken as a probabilistic generative model, and it enables imposing a prior that may identify independent factors generating word representation vectors. The probabilistic prior not only enhances the representation of word embedding, but also improves the model's robustness and stability. Furthermore, embodiments of the disclosed method may be flexibly plugged in various word embedding models. Extensive experimental results show that embodiments of the presented method may improve word representation on different tasks.