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Found 13 papers, 4 papers with code
Exploring Cultural Nuances in Emotion Perception Across 15 African Languages
In this work, we present a cross-linguistic analysis of emotion expression in 15 African languages.
Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages
As users express different emotions simultaneously in a single instance, annotating emotions in a multilabel setting such as the EthioEmo (Belay et al., 2025) dataset effectively captures this dynamic.
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text
We explore a thorough analysis of domain and task adaptive continual pretraining approaches for low-resource African languages and a promising result is shown for the evaluated tasks.
Whispering in Amharic: Fine-tuning Whisper for Low-resource Language
Training solely on new data leads to poor performance, but combining it with FLEURS data reinforces the model, enabling better specialization in Amharic.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding
We use this benchmark to evaluate several state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks.
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
The data instances are multi-labeled into six emotional classes, with additional datasets in 11 languages annotated for emotion intensity.
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages
These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes.
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding
The results show that accurate multi-label emotion classification is still insufficient even for high-resource languages such as English, and there is a large gap between the performance of high-resource and low-resource languages.
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding
In this work, we explore LLM evaluation challenges for low-resource language understanding and introduce ProverbEval, LLM evaluation benchmark for low-resource languages based on proverbs to focus on low-resource language understanding in culture-specific scenarios.
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models.
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
We compile an Amharic instruction fine-tuning dataset and fine-tuned LLaMA-2-Amharic model.
Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities
This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta.
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another.
