Search Results for author:
Found 15 papers, 7 papers with code
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
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
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.
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
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.
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).
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.
AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora.
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.
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.
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?
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and Yor\`ub\'a ) consisting of around 30, 000 annotated tweets per language (and 14, 000 for Nigerian-Pidgin), including a significant fraction of code-mixed tweets.
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.
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.
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.
