Dialect-Conditioned Neural Machine Translation for Gujarati Varieties Using Lightweight Adapters

2025 · Low-Resource NLP / Dialect Modeling

A Gujarati translation study that conditions NMT behavior for dialect variation using lightweight adapters, improving adaptability without fully retraining the base model.

Research Focus

Gujarati dialect adaptationLightweight adapter tuningLow-resource translation robustness

Publication Type

Research Paper

Area

Natural Language Processing

Summary

This work explores parameter-efficient neural machine translation for Gujarati varieties. By introducing lightweight adapters, the model can align to dialect-specific linguistic patterns while preserving the strengths of the base network. The framework reduces compute cost and supports practical deployment for low-resource translation scenarios.

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