AfriHG: News headline generation for African Languages
2024-12-28Code Available0· sign in to hype
Toyib Ogunremi, Serah Akojenu, Anthony Soronnadi, Olubayo Adekanmbi, David Ifeoluwa Adelani
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/dadelani/AfriHGOfficialnone★ 5
Abstract
This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 languages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multilingual mT5-base model. Finally, we show that the performance of fine-tuning AfriTeVa V2 with 313M parameters is competitive to prompting Aya-101 LLM with more than 13B parameters.