Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines.

翻译：多任务学习旨在通过同时训练多个任务来提升机器学习模型的性能与效率。然而，当前多任务学习研究面临两大挑战：1) 建模任务间关系以有效共享知识；2) 联合学习任务特定知识与共享知识。本文提出一种新型模型——自适应任务间融合网络（AdaTT），以同时应对上述挑战。AdaTT是一种深度融合网络，由多层任务特定融合单元与可选的共享融合单元构成。通过利用残差机制与门控机制实现任务间融合，这些单元能够自适应地学习共享知识与任务特定知识。为评估AdaTT性能，我们基于不同任务组在公共基准数据集与工业推荐数据集上开展实验。结果表明，AdaTT可显著超越现有最先进的基线模型。