Search Results for author:
Found 27 papers, 16 papers with code
FFR V1.0: Fon-French Neural Machine Translation
Africa has the highest linguistic diversity in the world.
FFR v1.1: Fon-French Neural Machine Translation
All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages.
Lanfrica: A Participatory Approach to Documenting Machine Translation Research on African Languages
Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general.
An Approach to Intelligent Pneumonia Detection and Integration
Each year, over 2. 5 million people, most of them in developed countries, die from pneumonia [1].
Crowd-sourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The case of Fon Language
Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge.
AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and Nobiin
From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing.
OkwuGbé: End-to-End Speech Recognition for Fon and Igbo
Our linguistic analyses (for Fon and Igbo) provide valuable insights and guidance into the creation of speech recognition models for other African low-resourced languages, as well as guide future NLP research for Fon and Igbo.
Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language
Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge.
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.
FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition
Using mel-spectrograms over conventional MFCCs features, we assess the abilities of convolutional neural networks to accurately recognize and classify emotions from speech data.
Biological Sequence Design with GFlowNets
In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.
MMTAfrica: Multilingual Machine Translation for African Languages
In this paper, we focus on the task of multilingual machine translation for African languages and describe our contribution in the 2021 WMT Shared Task: Large-Scale Multilingual Machine Translation.
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training?
Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisis causing the death of millions of people every year.
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.
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework.
Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage
Cancer is increasingly a global health issue.
Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages
In this work, we explore the applicability of low-compute approaches such as language adapters in the context of this low-resource double-bind.
MasakhaNEWS: News Topic Classification for African languages
Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API).
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.
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages
In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages.
AfriNames: Most ASR models "butcher" African Names
Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a laboratory result for a patient.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection
While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets.
FonMTL: Towards Multitask Learning for the Fon Language
Multitask learning is a learning paradigm that aims to improve the generalization capacity of a model by sharing knowledge across different but related tasks: this could be prevalent in very data-scarce scenarios.
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR
To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Study of Acquisition Functions for Medical Imaging Deep Active Learning
In this work, we show how active learning could be very effective in data scarcity situations, where obtaining labeled data (or annotation budget is very limited).
