Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
Table 4: Results using bilingual lexicons with varying sizes (40,000, 10,000, 2,000) and three languages. CorrNet W+N+Ch+L is the proposed approach with all the cluster types.
['[EMPTY]', '[EMPTY]', 'QVEC Monolingual', 'QVEC Multilingual', 'QVEC-CCA Monolingual', 'QVEC-CCA Multilingual']
In order to show the impact of the size of bilingual lexicons, we use three languages as a case study, and gradually reduce the size of the lexicons for each pair of languages from 40,000 to 10,000 and to 2,000. Both MultiCluster and MultiSkip by default take advantage of identical strings from any pair of languages when they learn the multilingual embeddings. For fair comparison, we thus use MultiCCA as a baseline. We observe that both MultiCCA and CorrNet approaches are sensitive to the size of the bilingual lexicons. Our approach on the other hand could maintain high performance, even when the bilingual lexicons were reduced to 2,000.