Faster decoding for subword level Phrase-based SMT between related languages
Table 3: Translation accuracy and Relative decoding time for character level translation using different decoding methods and parameters. Relative decoding time is indicated as a multiple of word-level decoding time. Translation accuracy and decode time per sentence for word-level decoding (in milliseconds) is shown on the last line for comparison.
['[EMPTY]', '[BOLD] Translation Accuracy  [BOLD] ben-hin', '[BOLD] Translation Accuracy  [BOLD] hin-mal', '[BOLD] Translation Accuracy  [BOLD] mal-hin', '[BOLD] Translation Accuracy  [BOLD] tel-mal', '[BOLD] Relative Decoding Time  [BOLD] ben-hin', '[BOLD] Relative Decoding Time  [BOLD] hin-mal', '[BOLD] Relative Decoding Time  [BOLD] mal-hin', '[BOLD] Relative Decoding Time  [BOLD] tel-mal']
While our experiments primarily concentrated on OS as the unit of translation, we also compared the performance of stack decoding and cube pruning for character lavel models. We see that character level models are 4-5 times slower than OS level models and hundreds of time slower than word level models with the default stack decoding. In the case of character based models also, the use of cube pruning (with pop-limit=1000) substantially speeds up decoding (20x speedup) with only a small drop in BLEU score.