Synthesizing Sanskrit Linguistics with NLP Models to Enhance Machine Translation Accuracy

The Intersection of Ancient Grammar and Modern NLP

Natural Language Processing (NLP) has made significant strides in recent years, yet challenges persist—particularly in translating low-resource languages. Sanskrit, with its highly structured grammar and precise syntactic rules, offers a unique opportunity to refine NLP models. By leveraging Panini's Ashtadhyayi, a 4th-century BCE treatise on Sanskrit grammar, researchers can enhance the robustness of machine translation systems for languages with limited digital corpora.

The Structural Advantages of Sanskrit

Sanskrit’s grammar is rule-based and agglutinative, making it computationally tractable. Key features include:

• Morphological Richness: Words are formed by combining roots, prefixes, and suffixes in predictable ways.
• Context-Free Syntax: Sandhi (euphonic combination) rules are deterministic, allowing for unambiguous parsing.
• Verb-Argument Structure: The language enforces strict subject-object-verb (SOV) ordering, reducing syntactic ambiguity.

Case Study: Applying Sanskrit’s Sandhi Rules to Neural Networks

A 2021 study by IIT Bombay demonstrated that integrating Sandhi-splitting algorithms into a transformer model improved segmentation accuracy for Tamil by 12%. This approach treats Sandhi rules as finite-state transducers, enabling better handling of agglutination in Dravidian languages.

Enhancing Low-Resource Language Translation

Most NLP models rely on large parallel corpora, which are scarce for languages like Bhojpuri or Gondi. Sanskrit’s grammatical framework provides a workaround:

• Rule-Based Transfer Learning: Use Sanskrit’s syntactic trees as a proxy for generating synthetic training data.
• Morpheme-Level Embeddings: Decompose words into morphemes (as in Sanskrit’s pratyaya system) to reduce vocabulary sparsity.

Empirical Results

Google’s 2022 adaptation of the mT5 model for Sanskrit-to-Hindi translation achieved a BLEU score of 34.2—comparable to high-resource pairs like French-English. The same architecture, when fine-tuned for Odiya (a low-resource language), saw a 9-point improvement over baseline models.

Challenges and Limitations

While promising, this synthesis faces hurdles:

• Lexical Gaps: Sanskrit lacks modern terminology, necessitating hybrid approaches.
• Computational Overhead: Parsing highly inflected forms increases latency by ~15% (per MIT 2023 benchmarks).

Future Directions

Ongoing research focuses on:

• Extending the framework to Austronesian languages.
• Developing quantum-optimized parsers for real-time Sandhi resolution.