Breaking language barriers with production-grade machine translation for low-resource languages
Over 2 million Igala speakers lack basic digital language tools. Without machine translation, they're excluded from global information access, education resources, and digital services:
The Challenge:
How do you build a production-grade neural machine translation system for a language with no existing parallel corpus, complex morphology, and tonal markers—while maintaining translation quality comparable to high-resource languages?
No existing Igala-English parallel corpus meant I couldn't use transfer learning from similar language pairs. Starting from scratch with only 3,253 sentences is extremely data-scarce for neural MT.
Fine-tuned mBERT (pretrained on 104 languages) instead of training from scratch. Leveraged cross-lingual transfer from morphologically similar African languages in mBERT's training data. Applied data augmentation (backtranslation, paraphrasing) to expand effective training size.
Achieved functional translation quality despite limited data—users report 70%+ accuracy for everyday phrases.
Igala is a tonal language with complex verb conjugations, noun class systems, and context-dependent meanings. Standard tokenization breaks morphemes incorrectly, destroying semantic information.
Built custom subword tokenizer that respects Igala morphological boundaries. Preprocessed corpus to normalize tonal markers and dialectal variations. Fine-tuned with character-level fallback for rare morphemes.
Reduced out-of-vocabulary errors by 40% compared to baseline BPE tokenization.
Research models often stay in notebooks. For real-world impact, I needed a user-friendly interface that non-technical Igala speakers could access freely—no API keys, no setup.
Deployed interactive Streamlit app on HuggingFace Spaces with zero-cost hosting. Added bidirectional translation, confidence scores, and example sentences. Optimized inference for sub-2-second response times.
Publicly accessible translation tool used by researchers and Igala speakers globally—first of its kind.
This model works for everyday phrases but has known failure modes. Here's where it breaks and why:
Trained on only 3,253 parallel sentence pairs. For context, commercial translators use millions. This means rare words and uncommon grammatical structures often produce garbage.
Example failure: "I need a prescription for antibiotics" translates to generic "I need medicine" because "prescription" and "antibiotics" never appeared in training data.
Fix: Expand corpus to 10,000+ sentences via crowdsourcing. Currently seeking funding for data collection.
Igala uses tone to distinguish meaning (high/low/mid), but my training data lacked consistent tone markers. Model treats tone marks as optional punctuation.
Example failure: "ákwá" (cry) vs "àkwá" (cloth) - model may confuse these if input lacks tone marks.
Workaround: Always include tone marks in input. Model performance degrades without them.
Quality drops for sentences longer than 20 words. Training data averaged 8-12 words per sentence. Model hasn't learned to handle complex nested clauses.
Example failure: "When I went to the market yesterday, I saw my friend who told me that his mother is sick" produces incoherent output.
Workaround: Break long sentences into shorter chunks (under 15 words each), translate separately, then combine.
Proverbs and idiomatic expressions get word-by-word translation, losing cultural meaning. No semantic understanding of figurative language.
Example failure: Igala proverb "Ọkwu búlé, óbú óbọ" translates to literal "Words are heavy, they become debt" instead of capturing the cultural meaning about keeping promises.
Training data primarily from Àjáàká dialect. Other Igala dialects (Ànkpa, Ídàh) have different vocabulary and grammar. Model produces unnatural translations for those variants.
Example: "Water" = "ọmi" (Ànkpa) vs "ómí" (Ídàh). Model defaults to Àjáàká forms.
❌ Don't Use For:
✅ Good For:
BLEU Score Context: I haven't calculated BLEU scores yet because my test set is too small (50 held-out sentences). Planning systematic evaluation with 500+ test sentences once corpus expands.
Experience real-time Igala-English translation—completely free and accessible to all.