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Chris Chinenye Emezue

5 accepted papers

2024

The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment

Chris Chinenye Emezue, Ifeoma Okoh, Chinedu Emmanuel Mbonu, Chiamaka Chukwuneke, Daisy Monika Lal, Ignatius Ezeani +8
COLING 2024main

The Igbo language is facing a risk of becoming endangered, as indicated by a 2025 UNESCO study. This highlights the need to develop language technologies for Igbo to foster communication, learning and preservation. To create robust, impactful, and widely adopted language technologies for Igbo, it is…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Cross-lingual Open-Retrieval Question Answering for African Languages

Odunayo Ogundepo, Tajuddeen Gwadabe, Clara E. Rivera, Jonathan H. Clark, Sebastian Ruder, David Ifeoluwa Adelani +38
EMNLP 2023long findings

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while s…

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2023

GFlowOut: Dropout with Generative Flow Networks

Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal +7
ICML 2023poster

Bayesian inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However, scaling Bayesian inference to large architectures is challenging and requires restrictive approximations. Monte Carlo D…

Cited by 24Source ↗PDF ↗Scholar ↗
2023

MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

Cheikh M. Bamba Dione, David Ifeoluwa Adelani, Peter Nabende, Jesujoba Alabi, Thapelo Sindane, Happy Buzaaba +38
ACL 2023long

In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both c…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel +39
EMNLP 2022main

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and re…

Cited by 31Source ↗PDF ↗code ↗Scholar ↗