activity
20192026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 720 across the 43 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.CL2022★ 3 cited

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…

cs.CL2022★ 2 cited

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…

eess.AS2022

BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus

Josh Meyer, David Ifeoluwa Adelani, Edresson Casanova +16

BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single sp…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…

cs.CL2022★ 1 cited

A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation

David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan +42

Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these dat…

cs.CL2022★ 51 cited

Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning

Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach +1

Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, t…