Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
Table 8: Name tagging performance (F-score, %) when the training set for the tagger was enhanced by annotated examples in other languages. CorrNet W+N+Ch+L is the proposed approach with all the cluster types.
['Train', 'Test', 'MultiCCA', 'CorrNet W', 'CorrNet W+N+Ch+L']
The multilingual embeddings learned by our approach consistently outperforms MultiCCA. More specifically, when there are not enough annotated examples, the performance could be improved by incorporating annotated examples from other languages. This is evident for Amharic, Tigrinya and Uighur.