DAL-GAN-PS is a dual-adversarial low-resource NLP framework for Sanskrit text generation using GANs, cross-lingual transfer learning, and PhonoScript linguistic constraints.
This project addresses one of the major challenges in Natural Language Processing (NLP): generating grammatically coherent text for morphologically rich low-resource languages.
The proposed framework, DAL-GAN-PS (Dual-Adversarial Linguistic GAN with PhonoScript Constraints), combines:
- Generative Adversarial Networks (GANs)
- Cross-lingual transfer learning
- Linguistic discriminators
- Rule-based phonological constraints
- Transformer encoder-decoder architectures
The framework is specifically designed for Sanskrit text generation under limited-data conditions while preserving:
- Morphological correctness
- Sandhi compliance
- Semantic coherence
- Grammatical fidelity
- Dual-adversarial GAN architecture
- Cross-lingual transfer from Hindi and Marathi
- Transformer encoder-decoder pretraining
- PhonoScript constraint module for Sanskrit grammar validation
- Semantic + linguistic discriminator pipelines
- Few-shot Sanskrit text generation
- Morphological and sandhi-aware decoding
- Research-focused low-resource NLP experimentation
The DAL-GAN-PS framework consists of three major stages:
A transformer-based encoder-decoder model is pretrained on Hindi-Marathi corpora and fine-tuned on Sanskrit datasets to transfer syntactic and morphological knowledge.
The system introduces two discriminators:
- Semantic Discriminator → evaluates fluency and semantic coherence
- Linguistic Discriminator → validates grammatical correctness using Sanskrit linguistic rules
A rule-based constraint engine using finite-state transducers (FSTs) validates:
- Sandhi formation
- Morphological agreement
- Syntactic correctness
- Sanskrit phonological structures
- Introduces a dual-adversarial GAN architecture for low-resource language generation
- Combines neural generation with rule-based linguistic validation
- Demonstrates effective cross-lingual transfer for Sanskrit NLP
- Improves grammatical accuracy and semantic consistency under limited training data
- Establishes a scalable framework for endangered and morphologically rich languages
| Metric | DAL-GAN-PS |
|---|---|
| BLEU Score | 42.67 |
| ROUGE-L | 54.90 |
| Morphological Accuracy | 91.2% |
| Sandhi Compliance | 88.9% |
| Expert Grammatical Correctness | 87.3% |
- Significant improvement over SeqGAN and TextGAN
- Faster convergence than Transformer NMT baselines
- Better grammatical consistency in Sanskrit text generation
- Strong semantic coherence under few-shot conditions
- Python
- PyTorch
- Transformer Models
- GAN Architectures
- NLP Pipelines
- Rule-Based Linguistic Systems
- Finite-State Transducers (FSTs)
.
├── pipeline.py
├── tokenizers.py
├── rouge.py
├── scoring.py
├── utils.py
├── trainer.sh
├── distributed_trainer.sh
├── requirements.txt
└── setup.pygit clone https://github.com/n-ikitasingh/DAL-GAN-PS.git
cd DAL-GAN-PS
pip install -r requirements.txtbash trainer.shbash distributed_trainer.shbash evaluate.sh- Sanskrit text generation
- Low-resource NLP research
- Linguistically constrained text generation
- Endangered language preservation
- Cross-lingual transfer learning
- Morphological analysis systems
DAL-GAN-PS: A Dual-Adversarial Linguistic GAN with PhonoScript Constraints
Published in: https://ieeexplore.ieee.org/document/11469818
2025 International Conference on Electrical, Communication, and Computing Technologies (iCONECCT 2025)
- Adapter-based LLM integration
- Expansion to Tamil and Navajo
- Dynamic constraint learning
- Multi-modal Sanskrit generation
- Educational Sanskrit AI tools
- Speech and handwritten text synthesis