15 citations · 47 across the 32 of their papers we have counts for
6 papers · 2 filters
Evaluation Sheet for Deep Research: A Use Case for Academic Survey Writing
Israel Abebe Azime, Tadesse Destaw Belay, Atnafu Lambebo Tonja
Large Language Models (LLMs) powered with argentic capabilities are able to do knowledge-intensive tasks without human involvement. A prime example of this tool is Deep research wi…
Bilingual Word Level Language Identification for Omotic Languages
Mesay Gemeda Yigezu, Girma Yohannis Bade, Atnafu Lambebo Tonja +3
Language identification is the task of determining the languages for a given text. In many real world scenarios, text may contain more than one language, particularly in multilingu…
A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script
Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Henok Biadglign Ademtew +4
Homophone normalization, where characters that have the same sound in a writing script are mapped to one character, is a pre-processing step applied in Amharic Natural Language Pro…
Explainable AI: XAI-Guided Context-Aware Data Augmentation
Melkamu Abay Mersha, Mesay Gemeda Yigezu, Atnafu Lambebo Tonja +4
Explainable AI (XAI) has emerged as a powerful tool for improving the performance of AI models, going beyond providing model transparency and interpretability. The scarcity of labe…
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan +32
Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey…
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages
Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto +12
This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their l…