Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Table 3: Ratio of triangulated to component phrase-table sizes. We use the size of larger of the component phrase-tables to compute the ratio.
['[BOLD] Lang Triple', '[BOLD] Word', '[BOLD] Morph', '[BOLD] BPE', '[BOLD] OS', '[BOLD] Char']
Subword-level pivot models are better than other units for two reasons. One, the underlying S-P and P-T translation models are better (e.g. 16% and 3% average improvement over word and morpheme-level models for OS). Two, the triangulation process involves an inner join on pivot language phrases common to the S-P and P-T phrase-tables. This causes data sparsity issues due to the large word and morpheme phrase-table vocabulary Dabre et al. More et al. On the other hand, the OS and BPE phrase-table vocabularies are smaller, so the impact of sparsity is limited. This effect can be observed by comparing the ratio of the triangulated phrase-table (S-P-T) with the component phrase-tables (S-P and P-T).