Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
Table 6: Name tagging result (F-score, %) using monolingual embedding and multilingual embeddings.
['[EMPTY]', 'Mono-', 'Multilingual', 'Multilingual CorrNet', 'Multilingual CorrNet']
For both Amharic and Turkish, the multilingual embeddings learned from our approach significantly improve over the monolingual embeddings, compared to MultiCCA. In the case of Uighur, all the multilingual embeddings fail to outperform the original monolingual embeddings. We conjecture that this is due to the use of Arabic script in Uighur, which differs from Turkish and English.