Search Results for author:
Found 23 papers, 15 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.
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Yet, there is little NLP research conducted on African languages.
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting
The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
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.
BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus
BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa.
TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models
Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the target domain is limited.
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages
However, text classification in low-resource languages is still challenging due to the lack of annotated data.
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?
MCSE: Multimodal Contrastive Learning of Sentence Embeddings
Learning semantically meaningful sentence embeddings is an open problem in natural language processing.
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision.
yosm: A new yoruba sentiment corpus for movie reviews
Several works on sentiment analysis have been done on high resource languages while low resources languages like Yoruba have been sidelined.
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.
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?
What can pre-trained multilingual sequence-to-sequence models like mBART contribute to translating low-resource languages?
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.
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e. g. their gender or ethnicity.
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.
Estimating Community Feedback Effect on Topic Choice in Social Media with Predictive Modeling
A defining feature of social media is that other users can provide feedback -- called community feedback -- to their content in the form of comments, replies, and retweets.
Social and Information Networks
Privacy Guarantees for De-identifying Text Transformations
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data.
On the effect of normalization layers on Differentially Private training of deep Neural networks
Differentially private stochastic gradient descent (DPSGD) is a variation of stochastic gradient descent based on the Differential Privacy (DP) paradigm, which can mitigate privacy threats that arise from the presence of sensitive information in training data.
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way.
Low Resource Named Entity Recognition
named-entity-recognition
+3
Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training
In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation.
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection
Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences.
