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Learning word embeddings has received a significant amount of attentionrecently. Often, word embeddings are learned in an unsupervised manner from alarge collection of text. The genre of the text typically plays an importantrole in the effectiveness of the resulting embeddings. How to effectively trainword embedding models using data from different domains remains a problem thatis underexplored. In this paper, we present a simple yet effective method forlearning word embeddings based on text from different domains. We demonstratethe effectiveness of our approach through extensive experiments on variousdown-stream NLP tasks.

A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings