BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning


Asa Cooper Stickland, Iain Murray ;
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5986-5995, 2019.


Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language understanding tasks in the GLUE benchmark have previously used transfer from a single large task: unsupervised pre-training with BERT, where a separate BERT model was fine-tuned for each task. We explore multi-task approaches that share a \hbox{single} BERT model with a small number of additional task-specific parameters. Using new adaptation modules, PALs or ‘projected attention layers’, we match the performance of separately fine-tuned models on the GLUE benchmark with $\approx$7 times fewer parameters, and obtain state-of-the-art results on the Recognizing Textual Entailment dataset.

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