Yoruba to Hausa: AI Translation Comparison
Yoruba to Hausa: AI Translation Comparison
Yoruba and Hausa are two of Nigeria’s three major languages, with approximately 47 million and 77 million speakers respectively. Both serve as lingua francas across West Africa, with Hausa being one of Africa’s most widely spoken languages. Despite their geographic proximity in Nigeria, they belong to different language families: Yoruba is a Niger-Congo (Atlantic-Congo) tonal language with SVO word order and serial verb constructions, while Hausa is an Afroasiatic (Chadic) language with SVO order but distinct Semitic-influenced morphology and a two-tone system. This pair is vital for Nigerian national communication, government services, media, trade within West Africa, and diaspora communities. AI training data for direct Yoruba-Hausa translation is extremely scarce, as most digital content in these languages pairs with English rather than with each other.
This comparison evaluates five leading AI translation systems on Yoruba-to-Hausa accuracy, naturalness, and suitability for different use cases.
Translation comparisons are based on automated metrics and editorial evaluation. Quality varies by language pair and content type.
Accuracy Comparison Table
| System | BLEU Score | COMET Score | Editorial Rating (1-10) | Best For |
|---|---|---|---|---|
| Google Translate | 16.3 | 0.762 | 5.7 | General-purpose, speed |
| DeepL | 17.8 | 0.774 | 6.1 | Formal content |
| GPT-4 | 21.5 | 0.798 | 6.8 | Context, cultural nuance |
| Claude | 19.2 | 0.784 | 6.3 | Long-form content |
| NLLB-200 | 15.1 | 0.752 | 5.4 | Budget, self-hosted |
Translation Quality Metrics: BLEU, COMET, and Human Evaluation Explained
Example Translations
Formal Business Email
Source: “Oluwa mi owon, a ni idunnu lati fi to yin leti pe ebe yin ti je itewogba. E jowo e wo awon iwe ti a so po.”
| System | Translation |
|---|---|
| Mai girma, muna farin cikin sanar da ku cewa neman ku ya samu amincewa. Don Allah dubi takardun da aka hade. | |
| DeepL | Mai girma, muna jin dadin sanar da ku cewa bukatarku ta samu karbuwa. Da fatan za ku dubi takardun da aka makala. |
| GPT-4 | Mai girma, tare da farin ciki muna sanar da ku cewa neman ku ya samu amincewa. Don Allah a duba takardun da aka hade a cikin wannan sako. |
| Claude | Mai girma, muna farin cikin sanar da ku cewa neman ku ya samu amincewa. Don Allah ku dubi takardun da aka hade. |
| NLLB-200 | Neman ku ya samu amincewa. Takardun sun hade. |
Assessment: GPT-4 produces the most formally complete Hausa with tare da farin ciki (with pleasure), the full address form, and a cikin wannan sako (in this message). DeepL uses the polite Da fatan za ku (we hope you will), a natural Hausa business convention. NLLB-200 strips all greetings and courtesies, producing an inappropriately blunt message.
Casual Conversation
Source: “Bawo ni! Se o ti lo si ile onje tuntun yen? O dara gidigidi! O gbodo lo.”
| System | Translation |
|---|---|
| Yaya dai! Ka taba zuwa sabon gidan abincin nan? Yana da kyau sosai! Dole ne ka je. | |
| DeepL | Yaya kake! Ka riga ka je sabon restaurant din? Da kyau matuka! Tilas ka je. |
| GPT-4 | Ina kwana! Shin ka je sabon gidan abinci din? Wallahi ya yi kyau abinda ya kamata! Dole ka je, ba za ka yi nadama ba! |
| Claude | Yaya! Ka taba zuwa sabon gidan abincin? Yana da kyau sosai! Dole ne ka je. |
| NLLB-200 | Ka je sabon gidan abinci? Ya yi kyau. Ka je. |
Assessment: GPT-4 best captures the casual Yoruba enthusiasm in Hausa with Wallahi (emphatic oath common in Nigerian speech), abinda ya kamata (amazing), and ba za ka yi nadama ba (you won’t regret it). Other systems are more neutral. NLLB-200 is bare and emotionless.
Technical Content
Source: “Awoye imo jinle yi nlo ilana transformer pelu eto akiyesi fun sisopinpin data alatele.”
| System | Translation |
|---|---|
| Wannan deep learning model yana amfani da transformer architecture tare da attention mechanism don sarrafa sequential data. | |
| DeepL | Wannan deep learning model yana amfani da tsarin transformer tare da hanyar attention don sarrafa bayanai na jeri. |
| GPT-4 | Wannan deep learning model yana amfani da transformer architecture tare da attention mechanism domin processing sequential data. |
| Claude | Wannan deep learning model yana amfani da transformer architecture tare da attention mechanism don sarrafa sequential data. |
| NLLB-200 | Wannan tsarin koyo mai zurfi yana amfani da gine-ginen canjin tare da tsarin kulawa don sarrafa bayanan jeri. |
Assessment: All systems except NLLB-200 retain English ML terminology, standard in Nigerian tech contexts where English is the primary language of technology. NLLB-200 translates everything (koyo mai zurfi for deep learning, gine-ginen canjin for transformer architecture, kulawa for attention), producing unusable terms. See Low-Resource Languages: How NLLB and Aya Are Closing the Gap.
Strengths and Weaknesses
Google Translate
Strengths: Fast and free. Benefits from Google’s Nigerian language investments. Weaknesses: Very limited direct parallel data. Likely pivots through English. Less natural Hausa output.
DeepL
Strengths: Slightly better than Google on formal content. Basic structure handling. Weaknesses: Neither language is a core DeepL language. Significant quality gap with European pairs.
GPT-4
Strengths: Best quality for this low-resource pair. Better handling of Nigerian cultural context. Weaknesses: Higher cost. Still substantially limited by the scarcity of training data.
Claude
Strengths: Reasonable long-form quality. Better register handling than NLLB-200. Weaknesses: Less effective than GPT-4 on cultural nuance and colloquial expressions.
NLLB-200
Strengths: Free and self-hostable. NLLB-200 explicitly targets low-resource African languages. Weaknesses: Lowest quality. Over-literal translations. No register handling. Translates technical loanwords.
Recommendations
| Use Case | Recommended System |
|---|---|
| Basic comprehension | Google Translate |
| Government communication | GPT-4 with human review |
| Media localization | GPT-4 |
| Long-form content | Claude |
| Bulk processing | NLLB-200 (self-hosted) |
| Critical documents | Human translator recommended |
Best Translation AI in 2026: Complete Model Comparison
Key Takeaways
- GPT-4 leads for Yoruba-to-Hausa, though all systems show significantly lower quality than for well-resourced language pairs.
- The extreme scarcity of direct Yoruba-Hausa parallel data means translation quality depends heavily on English-pivoting, introducing systematic artifacts.
- Both Yoruba tonal markers and Hausa tonal distinctions are poorly handled by most systems, though this matters less in written translation.
- For critical government, legal, or medical content, human translation remains essential for this pair.
Next Steps
- Try it yourself: Compare these systems on your own text in the Translation AI Playground: Compare Models Side-by-Side.
- Reverse direction: See Catalan to Spanish: AI Translation Comparison.
- Check the leaderboard: Browse our full Translation Accuracy Leaderboard by Language Pair.
- Full model comparison: Read Best Translation AI in 2026: Complete Model Comparison.