Search Results for author: Jesujoba O. Alabi

Found 9 papers, 6 papers with code

Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning

no code implementations COLING 2022 Ernie Chang, Jesujoba O. Alabi, David Ifeoluwa Adelani, Vera Demberg

The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition.

Text Generation

SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

1 code implementation14 Sep 2023 David Ifeoluwa Adelani, Hannah Liu, Xiaoyu Shen, Nikita Vassilyev, Jesujoba O. Alabi, Yanke Mao, Haonan Gao, Annie En-Shiun Lee

Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available datasets which excludes a large number of low-resource languages.

Cross-Lingual Transfer Language Modelling +4

YORC: Yoruba Reading Comprehension dataset

no code implementations18 Aug 2023 Anuoluwapo Aremu, Jesujoba O. Alabi, David Ifeoluwa Adelani

In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehension examination.

Cross-Lingual Transfer Reading Comprehension

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

1 code implementation COLING 2022 Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, Dietrich Klakow

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

NER Sentiment Analysis +5

Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi

1 code implementation5 Dec 2019 Jesujoba O. Alabi, Kwabena Amponsah-Kaakyire, David I. Adelani, Cristina España-Bonet

In this paper we focus on two African languages, Yor\`ub\'a and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing.

Test Word Embeddings

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