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In comparative linguistics, colexification refers to the phenomenon of alexical form conveying two or more distinct meanings. Existing work oncolexification patterns relies on annotated word lists, limiting scalabilityand usefulness in NLP. In contrast, we identify colexification patterns of morethan 2,000 concepts across 1,335 languages directly from an unannotatedparallel corpus. We then propose simple and effective methods to buildmultilingual graphs from the colexification patterns: ColexNet and ColexNet+.ColexNet's nodes are concepts and its edges are colexifications. In ColexNet+,concept nodes are additionally linked through intermediate nodes, eachrepresenting an ngram in one of 1,334 languages. We use ColexNet+ to train$\overrightarrow{\mbox{ColexNet+}}$, high-quality multilingual embeddings thatare well-suited for transfer learning. In our experiments, we first show thatColexNet achieves high recall on CLICS, a dataset of crosslingualcolexifications. We then evaluate $\overrightarrow{\mbox{ColexNet+}}$ onroundtrip translation, sentence retrieval and sentence classification and showthat our embeddings surpass several transfer learning baselines. Thisdemonstrates the benefits of using colexification as a source of information inmultilingual NLP.

Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs