Translating Morphologically Rich Indian Languages under Zero-Resource Conditions

Abstract

This work presents an in-depth analysis of machine translations of morphologically-rich Indo-Aryan and Dravidian languages under zero-resource conditions. It focuses on Zero-Shot Systems for these languages and leverages transfer-learning by exploiting target-side monolingual corpora and parallel translations from other languages. These systems are compared with direct translations using the BLEU and TER metrics. Further, Zero-Shot Systems are used as pre-trained models for fine-tuning with real human-generated data taken in different proportions that range from 100 sentences to the entire training set. Performances of the Indo-Aryan and Dravidian languages are compared with a focus on their morphological complexity. The systems with a Dravidian source language performed much better and reached very near to the level of direct translations. This is observed likely due to morphological richness and complexity in the language, which in turn provided more room for transfer-learning in this case. A comparative analysis based on language families has been done. These systems were fine-tuned further, which in turn outperformed direct translations with just 500 parallel sentences for a Dravidian source language. However, systems with an Indo-Aryan source language showed similar performance after getting fine-tuned with 10,000 sentences.