Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model called 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 a gating mechanism for task-to-task fusion, these units adaptively learn both shared knowledge and task-specific knowledge. To evaluate AdaTT's performance, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT significantly outperforms existing state-of-the-art baselines. Furthermore, our end-to-end experiments reveal that the model exhibits better performance compared to alternatives.

翻译：多任务学习（MTL）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，MTL研究面临两大挑战：1）有效建模任务间关系以实现知识共享；2）联合学习任务特定知识与共享知识。本文提出一种名为自适应任务间融合网络（AdaTT）的新型模型以应对上述挑战。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，我们在公开基准数据集与工业推荐数据集上使用多组任务开展实验。结果表明，AdaTT显著优于现有最先进基线方法。此外，端到端实验进一步揭示该模型相较替代方案展现出更优性能。