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Found 12 papers, 5 papers with code
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date.
URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base
URIEL is a knowledge base offering geographical, phylogenetic, and typological vector representations for 7970 languages.
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating computational costs associated with model capacity and data for fine-tuning.
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages.
A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base
In the pursuit of supporting more languages around the world, tools that characterize properties of languages play a key role in expanding the existing multilingual NLP research.
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency.
Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems
The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English.
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity
Fine-tuning and testing a multilingual large language model is expensive and challenging for low-resource languages (LRLs).
Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource Translation
In this paper, we show that intermediate-task fine-tuning (ITFT) of PMSS models is extremely beneficial for domain-specific NMT, especially when target domain data is limited/unavailable and the considered languages are missing or under-represented in the PMSS model.
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?
Neural Machine Translation for Low-Resource Languages: A Survey
Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase.
Unsupervised Transfer Learning via BERT Neuron Selection
To determine whether there are task-specific neurons that can be exploited for unsupervised transfer learning, we introduce a method for selecting the most important neurons to solve a specific classification task.
