Collaboration and Shared Tasks
Beyond standing communities, African NLP advances through organised collaboration: shared tasks, benchmarks, workshops, and open challenges that pool effort toward a common goal. For a field where no single group can cover hundreds of languages, these are the mechanism that turns scattered work into shared infrastructure.
Shared tasks and benchmarks
A shared task sets a common problem, a common dataset, and a common way of scoring, then invites everyone to take part. The effect multiplies effort: many teams build data and models for the same goal at once, and the field gets a benchmark to measure progress against afterward. African NLP has used this pattern repeatedly and well. AfriSenti became a SemEval shared task that produced sentiment datasets for fourteen languages and drew dozens of participating systems (Muhammad et al., 2023), the MasakhaNER efforts coordinated named-entity annotation across twenty languages (Adelani et al., 2022), and benchmarks like IrokoBench now give the community a shared yardstick for large language models on African languages. Contributing to a shared task is one of the most efficient ways to add a new language to the map, because you inherit the protocol, the tooling, and the company of others doing the same work.
Workshops and open challenges
Workshops and challenges are where the community gathers, plans, and recruits. The AfricaNLP workshop, held annually alongside major conferences, is the field's main venue for new African-language work, and the Deep Learning Indaba and its IndabaX events build the skills and relationships that make collaboration possible in the first place (Deep Learning Indaba). Open challenges turn this energy toward data directly. Zindi competitions and the AI4D African Language Dataset Challenge incentivised the creation of open datasets that did not exist before (Siminyu et al., 2021), and the Deep Learning Indaba's 2026 call for African datasets continues the pattern across text, speech, and vision. If you are starting out, joining one of these is often a better first move than working alone, because the community will help you build something that lasts.
