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Found 13 papers, 6 papers with code
PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English
In this work, we perform the first NLP work on the most popular variant of the language, providing three major contributions.
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin
Nigerian Pidgin is arguably the most widely spoken language in Nigeria.
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved.
MasakhaNER: Named Entity Recognition for African Languages
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Towards Best Practices for Training Multilingual Dense Retrieval Models
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research.
What a Creole Wants, What a Creole Needs
We demonstrate, through conversations with Creole experts and surveys of Creole-speaking communities, how the things needed from language technology can change dramatically from one language to another, even when the languages are considered to be very similar to each other, as with Creoles.
Intriguing Properties of Compression on Multilingual Models
Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages.
How Good are Commercial Large Language Models on African Languages?
We present a preliminary analysis of commercial large language models on two tasks (machine translation and text classification) across eight African languages, spanning different language families and geographical areas.
How good are Large Language Models on African Languages?
Our results suggest that all LLMs produce below-par performance on African languages, and there is a large gap in performance compared to high-resource languages like English most tasks.
Curry-DPO: Enhancing Alignment using Curriculum Learning & Ranked Preferences
With availability of such quality ratings for multiple responses, we propose utilizing these responses to create multiple preference pairs for a given prompt.
Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages
In this work, we challenge this assumption and present the first attempt at training a multilingual language model on only low-resource languages.
