Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
Table 7: Name tagging performance (F-score, %) when the tagger was trained on a source language and tested on a target language. CorrNet W+N+Ch+L is the proposed approach with all the cluster types.
['Train', 'Test', 'MultiCCA', 'CorrNet W', 'CorrNet W+N+Ch+L']
We further demonstrate the effectiveness of our multilingual embeddings on direct knowledge transfer. In this setting, we train a name tagger on one or two languages using multilingual embeddings and test it on a new language without any annotated data. For each testing language, our approach achieves better performance than MultiCCA and CorrNet. The closer that the languages are, such as Amharic and Tigrinya, and Turkish and Uighur, the better performance could be achieved, even when they may have distinct language scripts (e.g., Turkish and Uighur).