SemEval-2023 task 12: sentiment analysis for african languages (AfriSenti-SemEval)

SH Muhammad, I Abdulmumin, SM Yimam… - arXiv preprint arXiv …, 2023 - arxiv.org
We present the first Africentric SemEval Shared task, Sentiment Analysis for African
Languages (AfriSenti-SemEval)-The dataset is available at https://github. com/afrisenti …

Multilingual large language model: A survey of resources, taxonomy and frontiers

L Qin, Q Chen, Y Zhou, Z Chen, Y Li, L Liao… - arXiv preprint arXiv …, 2024 - arxiv.org
Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Aya dataset: An open-access collection for multilingual instruction tuning

S Singh, F Vargus, D Dsouza, BF Karlsson… - arXiv preprint arXiv …, 2024 - arxiv.org
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many
recent achievements in the space of natural language processing (NLP) can be attributed to …

MaChAmp at SemEval-2023 tasks 2, 3, 4, 5, 7, 8, 9, 10, 11, and 12: On the Effectiveness of Intermediate Training on an Uncurated Collection of Datasets.

R Van Der Goot - Proceedings of the 17th International Workshop …, 2023 - aclanthology.org
To improve the ability of language models to handle Natural Language Processing (NLP)
tasks and intermediate step of pre-training has recently beenintroduced. In this setup, one …

Zero-and few-shot prompting with llms: A comparative study with fine-tuned models for bangla sentiment analysis

MA Hasan, S Das, A Anjum, F Alam, A Anjum… - arXiv preprint arXiv …, 2023 - arxiv.org
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool
across diverse sectors such as marketing, politics, customer service, and healthcare. While …

Nlnde at semeval-2023 task 12: Adaptive pretraining and source language selection for low-resource multilingual sentiment analysis

M Wang, H Adel, L Lange, J Strötgen… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper describes our system developed for the SemEval-2023 Task 12" Sentiment
Analysis for Low-resource African Languages using Twitter Dataset". Sentiment analysis is …

The skipped beat: A study of sociopragmatic understanding in llms for 64 languages

C Zhang, KD Doan, Q Liao… - arXiv preprint arXiv …, 2023 - arxiv.org
Instruction tuned large language models (LLMs), such as ChatGPT, demonstrate remarkable
performance in a wide range of tasks. Despite numerous recent studies that examine the …

UMUTeam at SemEval-2023 task 12: Ensemble learning of LLMs applied to sentiment analysis for low-resource African languages

JA García-Díaz, C Caparros-Laiz… - Proceedings of the …, 2023 - aclanthology.org
These working notes summarize the participation of the UMUTeam in the SemEval 2023
shared task: AfriSenti, focused on Sentiment Analysis in several African languages. Two …

“I Searched for a Religious Song in Amharic and Got Sexual Content Instead'': Investigating Online Harm in Low-Resourced Languages on YouTube.

HH Nigatu, ID Raji - The 2024 ACM Conference on Fairness …, 2024 - dl.acm.org
Online social media platforms such as YouTube have a wide, global reach. However, little is
known about the experience of low-resourced language speakers on such platforms; …

Rehearsal-Free Modular and Compositional Continual Learning for Language Models

M Wang, H Adel, L Lange, J Strötgen… - arXiv preprint arXiv …, 2024 - arxiv.org
Continual learning aims at incrementally acquiring new knowledge while not forgetting
existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal …