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Interactive Refinement of Cross-Lingual Word Embeddings

Domaine:

natural language processing

Type de record:

papersoftwaremodel
Créateur:
YuaZhaVanFin
Hôte:avatar
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. EMNLP 2020; first two authors contribute equally

Visit

arxiv.org

Tasks

embeddings

Languages

Tigrigna

Tags

Computation and LanguageMachine Learning

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