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      "statement": "# MMoE: task-specific gates over shared expert networks\n\nAssigned a separate input-dependent gating network to each task while sharing expert subnetworks, letting different prediction towers use different mixtures of learned representations.\n\n## Scientific scope\n\nExtends mixture-of-experts for task-relationship modeling; it does not invent MoE or sparse conditional execution. Softmax mixtures need not select only one expert. Reduced negative transfer and trainability gains are empirical for studied task correlations, not guarantees for all task pairs.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJiaqi Ma; Zhe Zhao; Xinyang Yi; Jilin Chen; Lichan Hong; Ed H. Chi\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://raw.githubusercontent.com/wzhe06/Reco-papers/master/Multi-Task/%5BMMoE%5D%20Modeling%20Task%20Relationships%20in%20Multi-task%20Learning%20with%20Multi-gate%20Mixture-of-Experts.pdf)\n",
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