Why Bangla NLP is Hard
Bangla is the 7th most spoken language in the world, yet it's what NLP researchers call a "low-resource language." There are few labeled datasets, limited pretrained models, and the language's complex morphology makes tokenization non-trivial.
When I started building BanglaShorts—an AI platform that summarizes Bangladeshi news into 59-word micro-stories—I had to confront these challenges head-on.
The Data Problem
Most NLP progress assumes you have:
- Millions of labeled training examples
- Clean, well-formatted text corpora
- Established benchmarks for evaluation
For Bangla, none of this existed in the quantities I needed. Here's what I learned about making it work anyway.
Lesson 1: Start with the Right Pretrained Model
Not all multilingual models are equal for Bangla. I tested several:
| Model | Bangla Performance | Training Time |
| mBERT | Poor | 4x slower |
| XLM-R | Moderate | 3x slower |
| BanglaBERT | Excellent | Baseline |
BanglaBERT, specifically pretrained on Bangla text, outperformed multilingual models by a significant margin. The lesson: always check for language-specific pretrained models before reaching for multilingual ones.
Lesson 2: Tokenization Matters More Than You Think
Bangla has complex compound characters and conjuncts. Standard tokenizers often split these incorrectly:
- ক + ্ + ষ should be one token, not three
- Incorrect tokenization leads to poor subword representations
- I ended up training a custom SentencePiece tokenizer on 2M Bangla news articles
The custom tokenizer reduced vocabulary from 50k to 32k tokens while improving downstream task accuracy by 8%.
Lesson 3: Data Augmentation is Your Best Friend
With limited labeled data, augmentation becomes essential:
- Back-translation: Translate Bangla → English → Bangla using mT5
- Synonym replacement: Use Bangla WordNet for contextual synonym swaps
- Random deletion: Drop 10-15% of tokens randomly during training
- Code-mixing: Add English-Bangla mixed examples (common in real usage)
These techniques effectively tripled my training data and improved summarization ROUGE scores by 12%.
Lesson 4: mT5 for Summarization
For the summarization task specifically, mT5 (multilingual T5) fine-tuned on Bangla data worked best:
- Sequence-to-sequence architecture naturally fits summarization
- mT5's multilingual pretraining gives it a head start
- Fine-tuning with just 5,000 Bangla summary pairs gave production-quality results
The key was using BanglaBERT for classification/entity extraction and mT5 for generation—playing to each model's strengths.
Lesson 5: Evaluation is the Real Challenge
How do you evaluate Bangla summaries when standard metrics don't capture linguistic nuance?
- ROUGE scores are useful but insufficient for Bangla
- I built a custom evaluation pipeline combining ROUGE, BLEU, and human ratings
- Created a "Bangla Summary Quality" rubric with 5 dimensions: accuracy, fluency, informativeness, coherence, and conciseness
- Had native speakers rate 500 summaries to calibrate automatic metrics
Results
The final BanglaShorts system:
- Summarizes Bangla news articles into 59-word micro-stories
- 85% accuracy on factual correctness (vs. 72% with multilingual models)
- 70% reduction in news reading time for users
- Processes 500+ articles daily with automated pipeline
Key Takeaway
Building NLP systems for low-resource languages forces you to understand fundamentals deeply. You can't rely on pretrained pipelines to just work. Every decision—from tokenization to evaluation—requires careful consideration. The result? You become a better NLP engineer, and you build systems that genuinely serve underserved communities.