The conventional training approaches often face challenges in balancing the
breadth of multi-task learning (MTL) with the depth of single-task learning
(STL). To address this issue, we introduce the Multi-Task to Single-Task
(MT2ST) framework, a groundbreaking approach that can combine the
generalizability of MTL with the precision of STL. Our work include two
strategies: 'Diminish' and 'Switch'. 'Diminish' Strategy will gradually reduce
the influence of auxiliary tasks, while the 'Switch' strategy involves a shift
from multi-tasking to single-tasking at a specific timepoint at the training
process.
In this paper, we propose the Multi-Task to Single-Task (MT2ST) framework, a
novel approach that significantly enhances the efficiency and accuracy of word
embedding training while concurrently addressing prevalent issues such as
overfitting. Our empirical studies demonstrate that MT2ST can reduce training
time by 67\% when contrasted with single-task learning approaches, and by 13\%
compared to traditional multi-task learning methods. These findings underscore
MT2ST's potential to be a powerful tools for word embedding training
acceleration.

传统的训练方法在多任务学习 (MTL) 的广度与单任务学习 (STL) 的深度平衡方面常常面临挑战。为了解决这个问题，我们引入了多任务到单任务 (MT2ST) 框架，一种创新的方法，可以将 MTL 的普遍性与 STL 的精确性相结合。我们的工作包括两个策略：' 减弱 ' 和' 切换 '。' 减弱 ' 策略将逐渐减小辅助任务的影响，而 ' 切换 ' 策略则涉及在训练过程中的特定时间点从多任务转向单任务。