Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Table 4: Pivot vs. Direct translation (BLEU)
['[BOLD] Lang Triple', '[BOLD] Pivot  [BOLD] BPE', '[BOLD] Direct  [BOLD] BPE', '[BOLD] Direct  [BOLD] Word', '[BOLD] Direct  [BOLD] Morph', '[BOLD] Direct  [BOLD] OS', '[BOLD] Pivot  [BOLD] OS']
These subword-level pivot models outperform word-level direct models by 5-10%, which is encouraging. Remarkably, the subword-level pivot model is competitive with the morpheme-level models (about 95% of the morpheme BLEU score). The subword-level pivot models are competitive with the best performing direct counterparts too (about 90% of the direct system BLEU score). To put this fact in perspective, the BLEU scores of morpheme and word-level pivot systems are far below their corresponding direct systems (about 15% and 35% respectively). These observations strongly suggest that pivoting at the subword-level can better reconstruct the direct translation system than word and morpheme-level pivot systems.