Tchebycheff Procedure for Multi-task Text Classification
Yuren Mao, Shuang Yun, Weiwei Liu, Bo Du
Machine Learning for NLP Long Paper
Session 7B: Jul 7
(09:00-10:00 GMT)
Session 8B: Jul 7
(13:00-14:00 GMT)
Abstract:
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.
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