Tchebycheff Procedure for Multi-task Text Classification

Multi-task Learning methods have achieved significant progress in text classification. However, existing methods assume that multi-task text classification problems are convex multi-objective optimization problems, which is unrealistic in real-world applications. To address this issue, this paper presents a novel Tchebycheff procedure to optimize the multi-task classification problems without any convex assumption. The extensive experiments back up our theoretical analysis and validate the superiority of our proposals.