Scaling Assamese Regional NLP Translation to Millions of Daily Words
Building specialized machine learning pipelines for under-represented languages.
Problem Statement
Generic neural translation services like Google Translate frequently struggle with the subtle nuances, idioms, and multi-dialect vocabulary of the Assamese language. Our client needed a translation accuracy above 90% BLEU score for legal and educational publication materials.
Key Challenges
- !Lack of clean, standardized parallel corpora for Assamese-English pairs.
- !Complex grammatical cases and unique script characters of Assamese.
- !High latency issues when deploying deep translation models on conventional GPU instances.
The Solution
We curated a custom dataset of 1.5M parallel Assamese-English sentences and fine-tuned a custom Transformer-based sequence-to-sequence model. We implemented aggressive INT8 quantization and cached semantic word match queries on Redis, lowering response times to under 150ms per paragraph.
Our Development Process
- 1
Phase 1
Dataset collection, cleaning, and tokenization validation.
- 2
Phase 2
Training baseline translation models and adjusting hyper-parameters.
- 3
Phase 3
Building human-in-the-loop verification dashboards for dialect experts.
- 4
Phase 4
Optimization, containerization, and deployment to high-availability cloud instances.
Technologies Used
Quantitative Outcomes
- Achieved a BLEU score of 42.6, outperforming baseline public models by 28%.
- Served over 4 million translation tokens daily with a 99.9% API uptime.
- Lowered manual proofreading overheads for local news editors by 70%.
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Evaluating development pipelines launched by our engineers.
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