9 citations · 20 across the 5 of their papers we have counts for
7 papers · 1 filter
HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria
Saminu Mohammad Aliyu, Gregory Maksha Wajiga, Muhammad Murtala +3
Social media platforms allow users to freely share their opinions about issues or anything they feel like. However, they also make it easier to spread hate and abusive content. The…
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder +42
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability o…
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
Idris Abdulmumin, Michael Beukman, Jesujoba O. Alabi +8
We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the…
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud +7
Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context…
Enhanced back-translation for low resource neural machine translation using self-training
Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa
Improving neural machine translation (NMT) models using the back-translations of the monolingual target data (synthetic parallel data) is currently the state-of-the-art approach fo…
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model
Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa
An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-si…