Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language

TL;DR

This paper introduces neural network models for Sanskrit Sandhi formation and splitting, framing it as a sequence-to-sequence task, achieving superior accuracy without relying on external resources.

Contribution

It presents the first fully data-driven neural approach to Sanskrit Sandhi tasks, outperforming existing methods on standard datasets.

Findings

Higher accuracy than previous methods

Effective sequence-to-sequence modeling

No additional lexical resources used

Abstract

This paper describes neural network based approaches to the process of the formation and splitting of word-compounding, respectively known as the Sandhi and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to morphological analysis of Sanskrit texts. Sandhi leads to word transformations at word boundaries. The rules of Sandhi formation are well defined but complex, sometimes optional and in some cases, require knowledge about the nature of the words being compounded. Sandhi split or Vichchhed is an even more difficult task given its non uniqueness and context dependence. In this work, we propose the route of formulating the problem as a sequence to sequence prediction task, using modern deep learning techniques. Being the first fully data driven technique, we demonstrate that our model has an accuracy better than the existing methods on multiple standard datasets, despite not using any additional lexical or morphological resources. The code is being made available at https://github.com/IITD-DataScience/Sandhi_Prakarana