Chen, Chung ChiChung ChiChenHuang, Hen HsenHen HsenHuangHSIN-HSI CHEN2023-11-082023-11-082023-01-0116130073https://scholars.lib.ntu.edu.tw/handle/123456789/636978This paper focuses on the selection of hierarchical orders in multi-task architectures, a significant challenge in developing neural network architectures. We propose a systematic methodology based on the statistical results of the Apriori algorithm to arrange the order of co-training tasks. Our findings demonstrate that this approach can provide near-optimal performance, significantly reducing the exploration times in multi-task scenarios. The models developed using this methodology surpass state-of-the-art performances in flu vaccination intent prediction and music review sentiment analysis tasks, demonstrating its efficacy.demographic characterization | Exploration reduction | Hierarchical orderconference paper2-s2.0-85173544565https://api.elsevier.com/content/abstract/scopus_id/85173544565