Machine-translation engineers love big numbers: billions of sentence pairs, trillions of tokens, teraflops of compute. Sierra Leoneās languagesāKrio, Mende, Temne, Limba, Kono and others enter the conversation with almost none of that heft. Their digital footprints were, until recently, so small that most models simply skipped over them. Yet in the past three years the story has begun to change, and the shift is exposing a set of challengesāand ingenious fixesāthat go far beyond the act of swapping one word for another.
Finding something to measure
Before you can improve a system you need a yard-stick. In 2022 Meta AIās FLoRes-200 benchmark quietly slipped Krio (kri_Latn) and Mende (men_Latn) into its 200-language test suite. For the first time researchers could publish BLEU scores for Sierra Leoneās tongues that were directly comparable with French or Swahili, ending years of ad-hoc evaluation on home-made snippets.
Data scarcity and creative sourcing
Parallel corpora are virtually non-existent. Google Translateās addition of Krio in 2022 relied on a monolingual approach that teaches a model to translate without ever seeing a traditional sentence-aligned dataset. The company trained on raw web text and then validated with native speakers, proving that zero-shot and back-translation tricks can open the door for extremely low-resource dialects.
For Mende, usable audio appeared in an unexpected place: the Global Recordings Networkās archive of evangelistic programmes. More than five hours of narrated Bible stories, freely downloadable under a permissive licence, have become seed material for the first experimental speech-to-text models at Njala University.
Temne data arrived through journalism rather than scripture. A 2020 episode of the āMake Sierra Leone Famousā podcast captured Chief Bai Suba Bolt III recounting origin legends in Port Loko, providing a rare, high-quality recording of conversational Temne that volunteers have since begun to transcribe.
Limba benefits from an ongoing literacy and radio-broadcast programme run by Lutheran Bible Translators. The initiative produces fresh, annotated text every month, pairing printed primers with on-air readings and giving computational linguists a growing trickle of clean, contemporary prose.
Orthography: one language, many spellings
Text collection is only half the battle. Krioās writing system still fluctuates between phonemic spellings and English-influenced hybrids, and tone marks are used inconsistently. Temne writers disagree over whether to represent implosives with digraphs or special symbols, while Limba literacy classes are still standardising vowel length and nasal consonants. Tokenisers that work for English fragment these scripts or merge distinct words, inflating vocabulary size and confusing sub-word models.
Tiny models for tiny datasets
Massive multilingual transformers like NLLB-200 can translate Krio and Mende reasonably well, but their 3.3-billion-parameter footprint is hard to fine-tune on local hardware. South-African start-up Lelapa AI tackled the mismatch with InkubaLM, a 400-million-parameter model distilled specifically for African languages, including Krio. With careful adapter-layer training, community teams have reproduced similar results on single-GPU rigs proof that āsmallā can still be state-of-the-art when the data pipeline is tight.
Community as infrastructure
What Sierra Leone lacks in corpora it is beginning to make up in collaboration. The Masakhane network provides Slack forums, annotation tools and occasional GPU grants so that a student in Freetown can swap alignment scripts with a volunteer in Nairobi overnight. This living lab model lowers the barrier to entry for linguists who understand the language but not the code, while exposing software engineers to linguistic issues they would never meet in high-resource settings.
Where innovation must go next
The most urgent need is not bigger models but richer raw material. Market chatter, WhatsApp voice-notes and community-radio call-ins capture registers that scripture and formal news never touch. At the same time, orthography working groups have to settle at least provisionally on spellings that keyboards and tokenisers can adopt. Finally, all newly gathered text and audio must carry open, community-approved licences so local start-ups and NGOs can deploy translation or speech services without legal knots.
Beyond translation
When a Krio phrasebook appears in Google Translate or a Temne sentence lands intact in an NGO chatbot, it signals more than tech inclusion. It means the language can join national education portals, diaspora messaging apps and future AI assistants on equal terms. Building that reality is harder than translating a corpus; it requires solving sociolinguistic puzzles, inventing data pipelines from scratch and trusting small, shared models over shiny gigaton networks. Yet each verified dataset, each orthography agreement and each open-source checkpoint inches Sierra Leoneās languages toward a digital life that matches their vibrant, everyday speech.