本文主要将attention机制同时用在了task-task关系；feature-feature关系；task-feature关系当中。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Multiple Relational Attention Network for Multi-task Learning | |||||||||||
| 文献作者 | Fuzhen Zhuang; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| attention model; 多任务学习 | |||||||||||
| 摘要描述 | |||||||||||
| Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task relationships. In fact, these methods have not fully exploited the associated relationships during the learning process. On the one hand, replacing predefined task relationships by adaptively learned ones may result in higher prediction accuracy as it can avoid the risk of misguiding caused by improperly predefined relationships. On the other hand, apart from the task relationships, feature-task dependence and feature-feature interactions could also be employed to guide the learning process. Along this line, we propose a Multiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. Correspondingly, MRAN consists of three attention-based relationship learning modules: 1) a task-task relationship learning module which captures the relationships among tasks automatically and controls the positive and negative knowledge transfer adaptively; 2) a featurefeature interaction learning module that handles the complicated interactions among features; 3) a task-feature dependence learning module, which can associate the related features with target tasks separately. To evaluate the effectiveness of the proposed MARN, experiments are conducted on two public datasets and a real-world dataset crawled from a review hosting site. Experimental results demonstrate the superiority of our method over both classical and the state-of-the-art multi-task learning methods. | |||||||||||
