Compressing and interpreting word embeddings with latent space regularization and interactive semantics probing

Haoyu Li, Junpeng Wang, Yan Zheng, Liang Wang, Wei Zhang & Han-Wei Shen
Word embedding, a high-dimensional (HD) numerical representation of words generated by machine learning models, has been used for different natural language processing tasks, for example, translation between two languages. Recently, there has been an increasing trend of transforming the HD embeddings into a latent space (e.g. via autoencoders) for further tasks, exploiting various merits the latent representations could bring. To preserve the embeddings’ quality, these works often map the embeddings into an even higher-dimensional latent...
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