A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
This work addresses the challenge of domain adaptation in word embeddings for NLP applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of learning word embeddings from multiple domains by introducing a simple regularization-based algorithm, and demonstrates its effectiveness through extensive experiments on various downstream NLP tasks.
Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.