Summary of Swaquad-24: Qa Benchmark Dataset in Swahili, by Alfred Malengo Kondoro
SwaQuAD-24: QA Benchmark Dataset in Swahili
by Alfred Malengo Kondoro
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Swahili Question Answering (QA) benchmark dataset is designed to address the underrepresentation of Swahili in natural language processing. Building upon established benchmarks like SQuAD, GLUE, KenSwQuAD, and KLUE, this dataset will provide high-quality, annotated question-answer pairs that capture the linguistic diversity and complexity of Swahili. The dataset aims to support various applications such as machine translation, information retrieval, and social services like healthcare chatbots. Ethical considerations, including data privacy, bias mitigation, and inclusivity, are central to the dataset development. Future expansion plans include domain-specific content, multimodal integration, and broader crowdsourcing efforts. This innovation is expected to foster technological advancements in East Africa and provide a valuable resource for NLP research and applications in low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of data that helps computers answer questions in the Swahili language. Right now, there aren’t many resources like this for Swahili speakers, so this dataset will help bridge the gap. It’s designed to be used with various computer programs, such as translation tools and chatbots. The people making the dataset are also thinking about important issues like keeping people’s information private and making sure the data is fair and inclusive. They plan to add more features to the dataset in the future, including pictures and sounds. This will help make technology better for people who speak Swahili. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Question answering » Translation
