  Cross-lingual word embeddings transfer knowledge between languages: models
trained on high-resource languages can predict in low-resource languages. We
introduce CLIME, an interactive system to quickly refine cross-lingual word
embeddings for a given classification problem. First, CLIME ranks words by
their salience to the downstream task. Then, users mark similarity between
keywords and their nearest neighbors in the embedding space. Finally, CLIME
updates the embeddings using the annotations. We evaluate CLIME on identifying
health-related text in four low-resource languages: Ilocano, Sinhalese,
Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word
semantics and have higher test accuracy than the original embeddings. CLIME
often improves accuracy faster than an active learning baseline and can be
easily combined with active learning to improve results.
