Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Computation and Language
2023-12-12 v1 Machine Learning
Abstract
In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance. We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resource tasks. We provide a thorough empirical study and analysis of this method's benefits showing that it achieves consistent improvements relative to the performance trade-off profile of standard static weighting. We analyze under what data regimes this method is applicable and show its improvements empirically in neural machine translation (NMT) and multi-lingual language modeling.
Keywords
Cite
@article{arxiv.2312.06134,
title = {Order Matters in the Presence of Dataset Imbalance for Multilingual Learning},
author = {Dami Choi and Derrick Xin and Hamid Dadkhahi and Justin Gilmer and Ankush Garg and Orhan Firat and Chih-Kuan Yeh and Andrew M. Dai and Behrooz Ghorbani},
journal= {arXiv preprint arXiv:2312.06134},
year = {2023}
}