Summary of State Of Nlp in Kenya: a Survey, by Cynthia Jayne Amol et al.
State of NLP in Kenya: A Survey
by Cynthia Jayne Amol, Everlyn Asiko Chimoto, Rose Delilah Gesicho, Antony M. Gitau, Naome A. Etori, Caringtone Kinyanjui, Steven Ndung’u, Lawrence Moruye, Samson Otieno Ooko, Kavengi Kitonga, Brian Muhia, Catherine Gitau, Antony Ndolo, Lilian D. A. Wanzare, Albert Njoroge Kahira, Ronald Tombe
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey provides a comprehensive assessment of Natural Language Processing (NLP) in Kenya, highlighting ongoing efforts in dataset creation, machine translation, sentiment analysis, and speech recognition for underrepresented indigenous languages like Kiswahili, Dholuo, Kikuyu, and Luhya. The development of NLP in Kenya is constrained by limited resources and tools, resulting in the underrepresentation of most indigenous languages in digital spaces. The paper uncovers significant gaps by evaluating existing datasets and NLP models, emphasizing the need for large-scale language models and sufficient digital representation of Indigenous languages. Key applications like machine translation, information retrieval, and sentiment analysis are analyzed to address local linguistic needs. The study also explores governance, policies, and regulations shaping the future of AI and NLP in Kenya and proposes a strategic roadmap for guiding future research and development efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers understand African languages, like Kiswahili or Kikuyu, and how that can help people in Kenya. Right now, there aren’t many tools to help with this, so researchers are working hard to create more datasets and language models. They want to make sure these languages are represented well online. The paper talks about what’s missing and what needs to change to make progress happen. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Translation
