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Achieving state-of-the-art performance on natural language understandingtasks typically relies on fine-tuning a fresh model for every task.Consequently, this approach leads to a higher overall parameter cost, alongwith higher technical maintenance for serving multiple models. Learning asingle multi-task model that is able to do well for all the tasks has been achallenging and yet attractive proposition. In this paper, we propose\textsc{HyperGrid}, a new approach for highly effective multi-task learning.The proposed approach is based on a decomposable hypernetwork that learnsgrid-wise projections that help to specialize regions in weight matrices fordifferent tasks. In order to construct the proposed hypernetwork, our methodlearns the interactions and composition between a global (task-agnostic) stateand a local task-specific state. We apply our proposed \textsc{HyperGrid} onthe current state-of-the-art T5 model, demonstrating strong performance acrossthe GLUE and SuperGLUE benchmarks when using only a single multi-task model.Our method helps bridge the gap between fine-tuning and multi-task learningapproaches.

HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections