Multi-task learning (MTL) aims at achieving a better model by leveraging data
and knowledge from multiple tasks. However, MTL does not always work --
sometimes negative transfer occurs between tasks, especially when aggregating
loosely related skills, leaving it an open question when MTL works. Previous
studies show that MTL performance can be improved by algorithmic tricks.
However, what tasks and skills should be included is less well explored. In
this work, we conduct a case study in Financial NLP where multiple datasets
exist for skills relevant to the domain, such as numeric reasoning and
sentiment analysis. Due to the task difficulty and data scarcity in the
Financial NLP domain, we explore when aggregating such diverse skills from
multiple datasets with MTL can work. Our findings suggest that the key to MTL
success lies in skill diversity, relatedness between tasks, and choice of
aggregation size and shared capacity. Specifically, MTL works well when tasks
are diverse but related, and when the size of the task aggregation and the
shared capacity of the model are balanced to avoid overwhelming certain tasks.

该论文研究了多任务学习在金融自然语言处理领域中的应用，发现多任务学习成功的关键在于技能的多样性、任务的关联性以及模型共享能力的平衡，提出在任务多样但相关、任务聚合尺度和模型共享能力平衡的情况下，多任务学习可以发挥其优势。

多任务学习整合多项技能何时有效？金融 NLP 案例研究

When Does Aggregating Multiple Skills with Multi-Task Learning Work? A  Case Study in Financial NLP

In real-world environments, robots need to be resilient to damages and robust
to unforeseen scenarios. Quality-Diversity (QD) algorithms have been
successfully used to make robots adapt to damages in seconds by leveraging a
diverse set of learned skills. A high diversity of skills increases the chances
of a robot to succeed at overcoming new situations since there are more
potential alternatives to solve a new task.However, finding and storing a large
behavioural diversity of multiple skills often leads to an increase in
computational complexity. Furthermore, robot planning in a large skill space is
an additional challenge that arises with an increased number of skills.
Hierarchical structures can help reducing this search and storage complexity by
breaking down skills into primitive skills. In this paper, we introduce the
Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural
repertoire to learn diverse skills and leverages them to make the robot adapt
quickly in the physical world. We show that the hierarchical decomposition of
skills enables the robot to learn more complex behaviours while keeping the
learning of the repertoire tractable. Experiments with a hexapod robot show
that our method solves a maze navigation tasks with 20% less actions in
simulation, and 43% less actions in the physical world, for the most
challenging scenarios than the best baselines while having 78% less complete
failures.

使用 Hierarchical Trial and Error 算法，将 QD 算法分层结构化以增加机器人学习技能的多样性，并使机器人能够更快地适应物理世界，实验表明，该方法优于现有基准算法，可提高机器人迎接新挑战的能力。