Exploration Reduction by Selecting a Hierarchical Order of Implicit Author Demographic Characterizations
Journal
CEUR Workshop Proceedings
Journal Volume
3477
Date Issued
2023-01-01
Author(s)
Abstract
This 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.
Subjects
demographic characterization | Exploration reduction | Hierarchical order
Type
conference paper