Multilingual Neural Machine Translation System for Indic to Indic
  Languages
Sudhansu Bala Das Divyajyoti Panda Tapas Kumar Mishra Bidyut Kr. Patra Asif Ekbal
This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs
implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All
the models are evaluated using the BLEU score. In addition, the languages are
classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and
West Indo-Aryan (WI). The effect of language relatedness on MNMT model
efficiency is studied. Owing to the presence of large corpora from English (EN)
to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To
achieve this, English- Indic (EN-IL) models are also developed, with and
without the usage of related languages. Results reveal that using related
languages is beneficial for the WI group only, while it is detrimental for the
EI group and shows an inconclusive effect on the DR group, but it is useful for
EN-IL models. Thus, related language groups are used to develop pivot MNMT
models. Furthermore, the IL corpora are transliterated from the corresponding
scripts to a modified ITRANS script, and the best MNMT models from the previous
approaches are built on the transliterated corpus. It is observed that the
usage of pivot models greatly improves MNMT baselines with AS-TA achieving the
minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS,
ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the
best. Transliteration also helps the models with few exceptions. The best
increment of scores is observed in ML, TA, and BN and the worst average
increment is observed in KN, HI, and PA, across all languages. The best model
obtained is the PA-HI language pair trained on PAWI transliterated corpus which
gives 24.29 BLEU.