Shuang Yun


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Tchebycheff Procedure for Multi-task Text Classification
Yuren Mao | Shuang Yun | Weiwei Liu | Bo Du
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multi-task Learning methods have achieved great progress in text classification. However, existing methods assume that multi-task text classification problems are convex multiobjective 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 convex assumption. The extensive experiments back up our theoretical analysis and validate the superiority of our proposals.