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Cross-lingual word embeddings encode the meaning of words from differentlanguages into a shared low-dimensional space. An important requirement formany downstream tasks is that word similarity should be independent of language- i.e., word vectors within one language should not be more similar to eachother than to words in another language. We measure this characteristic usingmodularity, a network measurement that measures the strength of clusters in agraph. Modularity has a moderate to strong correlation with three downstreamtasks, even though modularity is based only on the structure of embeddings anddoes not require any external resources. We show through experiments thatmodularity can serve as an intrinsic validation metric to improve unsupervisedcross-lingual word embeddings, particularly on distant language pairs inlow-resource settings.

A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity