This paper examines an online multi-task learning (OMTL) method, which
processes data sequentially to predict labels across related tasks. The
framework learns task weights and their relatedness concurrently. Unlike
previous models that assumed static task relatedness, our approach treats tasks
as initially independent, updating their relatedness iteratively using newly
calculated weight vectors. We introduced three rules to update the task
relatedness matrix: OMTLCOV, OMTLLOG, and OMTLVON, and compared them against a
conventional method (CMTL) that uses a fixed relatedness value. Performance
evaluations on three datasets a spam dataset and two EEG datasets from
construction workers under varying conditions demonstrated that our OMTL
methods outperform CMTL, improving accuracy by 1\% to 3\% on EEG data, and
maintaining low error rates around 12\% on the spam dataset.

本研究探讨了一种在线多任务学习（OMTL）方法，该方法按顺序处理数据以预测相关任务的标签，并同时学习任务权重及其相互关系。通过引入三种规则来更新任务相互关系矩阵：OMTLCOV、OMTLLOG 和 OMTLVON，与使用固定相关性值的传统方法（CMTL）相比较，我们的方法在三个数据集上进行的性能评估表明，OMTL 方法在 EEG 数据上将准确率提高了 1％至 3％，并在垃圾邮件数据集上保持了大约 12％的低错误率。

多任务及其关系的在线学习：针对垃圾邮件数据和建筑领域脑电信号的测试

Online Learning of Multiple Tasks and Their Relationships : Testing on  Spam Email Data and EEG Signals Recorded in Construction Fields

Multi-task learning (MTL) has become increasingly popular in natural language
processing (NLP) because it improves the performance of related tasks by
exploiting their commonalities and differences. Nevertheless, it is still not
understood very well how multi-task learning can be implemented based on the
relatedness of training tasks. In this survey, we review recent advances of
multi-task learning methods in NLP, with the aim of summarizing them into two
general multi-task training methods based on their task relatedness: (i) joint
training and (ii) multi-step training. We present examples in various NLP
downstream applications, summarize the task relationships and discuss future
directions of this promising topic.

本文对近期自然语言处理领域中的多任务学习方法进行了调查，总结了两种基于任务相关性的多任务训练方法：联合训练和多步训练。文章对自然语言处理领域中几个下游应用案例进行了说明，总结了任务关系，并讨论了未来的研究方向。