Hindi to Dravidian Language Neural Machine Translation Systems
Vijay Sundar Ram, Sobha Lalitha Devi · 2023
Neural machine translation (NMT) has achieved state-of-art performance in highresource language pairs, but the performance of NMT drops in lowresource conditions.Morphologically rich languages are yet another challenge in NMT.The common strategy to handle this issue is to apply sub-word segmentation.In this work, we compare the morphologically inspired segmentation methods against the Byte Pair Encoding (BPE) in processing the input for building NMT systems for Hindi to Malayalam and Hindi to Tamil, where Hindi is an Indo-Aryan language and Malayalam and Tamil are south Dravidian languages.These two languages are low resource, morphologically rich and agglutinative.Malayalam is more agglutinative than Tamil.We show that for both the language pairs, the morphological segmentation algorithm out-performs BPE.We also present an elaborate analysis on translation outputs from both the NMT systems.