TOMATO: A Topic-Wise Multi-Task Sparsity Model

Jason Jiasheng Zhang, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The Multi-Task Learning (MTL) leverages the inter-relationship across tasks and is useful for applications with limited data. Existing works articulate different task relationship assumptions, whose validity is vital to successful multi-task training. We observe that, in many scenarios, the inter-relationship across tasks varies across different groups of data (i.e., topic), which we call within-topic task relationship hypothesis. In this case, current MTL models with homogeneous task relationship assumption cannot fully exploit different task relationships among different groups of data. Based on this observation, in this paper, we propose a generalized topic-wise multi-task architecture, to capture the within-topic task relationship, which can be combined with any existing MTL designs. Further, we propose a new specialized MTL design, topic-task-sparsity, along with two different types of sparsity constraints. The architecture, combined with the topic-task-sparsity design, constructs our proposed TOMATO model. The experiments on both synthetic and 4 real-world datasets show that our proposed models consistently outperform 6 state-of-the-art models and 2 baselines with improvement from $5%$ to $46%$ in terms of task-wise comparison, demonstrating the validity of the proposed within-topic task relationship hypothesis. We release the source codes and datasets of TOMATO at:

Original languageEnglish (US)
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450368599
StatePublished - Oct 19 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: Oct 19 2020Oct 23 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Business, Management and Accounting
  • General Decision Sciences


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