  Out-of-vocabulary (OOV) words can pose serious challenges for machine
translation (MT) tasks, and in particular, for low-resource language (LRL)
pairs, i.e., language pairs for which few or no parallel corpora exist. Our
work adapts variants of seq2seq models to perform transduction of such words
from Hindi to Bhojpuri (an LRL instance), learning from a set of cognate pairs
built from a bilingual dictionary of Hindi--Bhojpuri words. We demonstrate that
our models can be effectively used for language pairs that have limited
parallel corpora; our models work at the character level to grasp phonetic and
orthographic similarities across multiple types of word adaptations, whether
synchronic or diachronic, loan words or cognates. We describe the training
aspects of several character level NMT systems that we adapted to this task and
characterize their typical errors. Our method improves BLEU score by 6.3 on the
Hindi-to-Bhojpuri translation task. Further, we show that such transductions
can generalize well to other languages by applying it successfully to Hindi --
Bangla cognate pairs. Our work can be seen as an important step in the process
of: (i) resolving the OOV words problem arising in MT tasks, (ii) creating
effective parallel corpora for resource-constrained languages, and (iii)
leveraging the enhanced semantic knowledge captured by word-level embeddings to
perform character-level tasks.
