Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual
  Translation of Dravidian Languages
Danish Ebadulla Rahul Raman S. Natarajan Hridhay Kiran Shetty Ashish Harish Shenoy
Current research in zero-shot translation is plagued by several issues such
as high compute requirements, increased training time and off target
translations. Proposed remedies often come at the cost of additional data or
compute requirements. Pivot based neural machine translation is preferred over
a single-encoder model for most settings despite the increased training and
evaluation time. In this work, we overcome the shortcomings of zero-shot
translation by taking advantage of transliteration and linguistic similarity.
We build a single encoder-decoder neural machine translation system for
Dravidian-Dravidian multilingual translation and perform zero-shot translation.
We compare the data vs zero-shot accuracy tradeoff and evaluate the performance
of our vanilla method against the current state of the art pivot based method.
We also test the theory that morphologically rich languages require large
vocabularies by restricting the vocabulary using an optimal transport based
technique. Our model manages to achieves scores within 3 BLEU of large-scale
pivot-based models when it is trained on 50\% of the language directions.