Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Table 1: Comparison of triangulation for various translation units (BLEU). Lang triple refers to the source-pivot-target languages. Scores in bold indicate highest values for the language triple. † means difference between OS and BPE scores is statistically significant (p<0.05). NA: OS segmentations cannot be done for Urdu. The last row shows average change in BLEU scores for word, morpheme and character-level model compared to the OS and BPE-level models.
['[BOLD] Lang Triple', '[BOLD] Word', '[BOLD] Morph', '[BOLD] BPE', '[BOLD] OS', '[BOLD] Char']
We observe that the OS and BPE-level pivot models significantly outperform word, morpheme and character-level pivot models (average improvements above 55% over word-level and 14% over morpheme-level). The greatest improvement is observed when the source and target languages belong to different families (though they have a contact relationship), showing that subword-level models can utilize the lexical similarity between languages. Translation between agglutinative Dravidian languages also shows a major improvement. The OS and BPE models are comparable in performance. However, unlike OS, BPE segmentation can also be applied to translations involving languages with non-alphabetic scripts (like Urdu) and show significant improvement in those cases also. Evaluation with LeBLEU Virpioja and Grönroos For brevity, we report BLEU scores in subsequent experiments.