To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models. Different from previous methods, word embedding is taken as a probabilistic generative model, and it enables us to impose a prior regularizing word representation learning. The proposed prior not only enhances the representation of embedding vectors but also improves the model's robustness and stability. The structure of the proposed prior is simple and effective, and it can be easily implemented and flexibly plugged in most existing word embedding models. Extensive experiments show the proposed method improves word representation on various tasks.

翻译：为改进词表示学习，我们提出了一种概率先验，该先验能够无缝集成到词嵌入模型中。与先前方法不同，我们将词嵌入视为概率生成模型，从而能够施加正则化词表示学习的先验。所提出的先验不仅增强了嵌入向量的表示能力，还提升了模型的鲁棒性与稳定性。该先验结构简单且有效，易于实现并可灵活嵌入到大多数现有词嵌入模型中。大量实验表明，所提方法能在多种任务上改善词表示。