Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Table 5: Combination of multiple pivots (BLEU). Pivots used for (i) mar-ben: guj, hin, pan (ii) mal-hin: tel, mar, guj. Best pivot language indicated in brackets. Statistically significant difference from direct is indicated for: all pivots(†) and direct+all pivots(‡) (p<0.05).
['[BOLD] Model', '[BOLD] mar-ben  [BOLD] OS', '[BOLD] mar-ben  [BOLD] BPE', '[BOLD] mal-hin  [BOLD] OS', '[BOLD] mal-hin  [BOLD] BPE']
We investigated if combining multiple pivot translation models can be a substitute for the direct translation model. Direct model refers to translation system built using the source-target parallel corpus. Using linear interpolation with equal weights, we combined pivot translation models trained on different pivot languages. Previous studies have shown that word and morpheme-level multiple pivot systems were not competitive with the direct system, possibly due to the effect of sparsity on triangulation More et al. Our results show that once the ill-effects of data sparsity are reduced due to the use of subword models, multiple pivot languages can maximize translation performance because: (i) they bring in more translation options, and (ii) they improve the estimates of feature values with evidence from multiple languages. Linear interpolation of the direct system with all the pivot systems with equal interpolation weights also benefitted the translation system. Thus, multilinguality helps overcome the lack of parallel corpora between the two languages.