论文标题
什么和地点：通过NAS学习插入适配器以进行多域学习
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning
论文作者
论文摘要
作为一个重要且具有挑战性的问题，多域学习（MDL）通常会寻求一组有效的轻质域特异性适配器模块，这些模块插入了一个共同的域 - 不合SNOSTIC网络。通常，在模型学习之前，对所有域进行了手工制作和修复适配器堵塞和结构设计的方式，从而导致学习的僵化和计算强度。通过这种动机，我们建议使用神经体系结构搜索（NAS）学习一个数据驱动的适配器插入策略，该策略会自动确定这些适配器模块的位置。此外，我们在NAS驱动的学习方案中提出了一个用于适配器结构设计的NAS适配器模块，该模块自动发现了不同域的有效适配器模块结构。实验结果证明了在可比性能的条件下，我们的MDL模型对现有方法的有效性。
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in the learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with Neural Architecture Search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose a NAS-adapter module for adapter structure design in a NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.
