Code-Mixed to Monolingual Translation Framework
Table 2: Error contribution.
['[BOLD] Module', '[BOLD] Contribution']
Exp 1. We randomly took 100 instances where BLEU score achieved was less than 15. Then we fed this back to our pipeline and collected outputs from each of the modules. We manually associated each of the errors with the respective module causing it, considering the input to it was correct. Language tagger being the starting module in our pipeline requires the most improvement for better results followed by the machine translation system and the back-transliteration module. All of these are supervised models and can be improved with more training data. Exp 2. A linguist proficient in both English and Bengali manually divided our test data into two sets, one where the matrix language was Bengali (MBn) and the other where matrix language was English (MEn). The size of (MBn) was 1205 and for (MEn) it was 395. When feeding the sets separately to CMT2, the BLEU and TER score achieved on MBn was 16.98 and 55.02 while on MEn it was 9.3 and 65.11 respectively. This problem can be easily solved if matrix and embedded languages are identified first, and then passed on to different systems accordingly, i.e. one for (MBn) type, and one for (MEn) type.