  Neural models for text generation require a softmax layer with proper token
embeddings during the decoding phase. Most existing approaches adopt single
point embedding for each token. However, a word may have multiple senses
according to different context, some of which might be distinct. In this paper,
we propose KerBS, a novel approach for learning better embeddings for text
generation. KerBS embodies two advantages: (a) it employs a Bayesian
composition of embeddings for words with multiple senses; (b) it is adaptive to
semantic variances of words and robust to rare sentence context by imposing
learned kernels to capture the closeness of words (senses) in the embedding
space. Empirical studies show that KerBS significantly boosts the performance
of several text generation tasks.
