The learning of domain-common embeddings can allow the ad-vantage of data augmentation of common semantics of multiple domains, and meanwhile, domainspecific embeddings allow us to capture the varied semantics of specific words in different domains.
for each word in the vocabulary, we design a distribution to depict the probability of the word being domain-common.