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Found 9 papers, 6 papers with code
Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning
The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition.
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects
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.
YORC: Yoruba Reading Comprehension dataset
In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehension examination.
AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users.
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task.
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages.
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
We investigate how and when this training condition affects the final quality and intelligibility of a translation.
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
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.
