Search Results for author:
Found 7 papers, 1 papers with code
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 Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems
We employ a pre-trained LLaMA2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese.
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.
