Code-Mixed to Monolingual Translation Framework
Table 1: Evaluation results.
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] TER', '[BOLD] Adq.', '[BOLD] Flu.']
Two variants of our system was tested, one without token reordering (CMT1) and one with (CMT2). Manual scoring (in the range 1-5, low to high quality) of Adequacy and Fluency banchs:2015adequacy was done by a bi-lingual linguist, fluent in both En and Bn, with Bn as mother tongue. We can clearly see that our pipeline outperforms GNMT by a fair margin (about 13.34 BLEU, 18.34 TER) and token reordering further improves our system, especially in the case of fluency. Also, for a deeper analysis, we performed two experiments using CMT2.