Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models

• URL: http://arxiv.org/abs/2403.19709v1
• Date: Mon, 25 Mar 2024 17:21:56 GMT
• Title: Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models
• Authors: Tsendsuren Munkhdalai, Youzheng Chen, Khe Chai Sim, Fadi Biadsy, Tara Sainath, Pedro Moreno Mengibar,
• Abstract summary: We introduce an adapter module that has a better efficiency in large scale multi-task adaptation scenario. The adapter consists of a single shared controller network and multiple task-level adapter heads.
• Score: 12.230087530720652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter efficient adaptation methods have become a key mechanism to train large pre-trained models for downstream tasks. However, their per-task parameter overhead is considered still high when the number of downstream tasks to adapt for is large. We introduce an adapter module that has a better efficiency in large scale multi-task adaptation scenario. Our adapter is hierarchical in terms of how the adapter parameters are allocated. The adapter consists of a single shared controller network and multiple task-level adapter heads to reduce the per-task parameter overhead without performance regression on downstream tasks. The adapter is also recurrent so the entire adapter parameters are reused across different layers of the pre-trained model. Our Hierarchical Recurrent Adapter (HRA) outperforms the previous adapter-based approaches as well as full model fine-tuning baseline in both single and multi-task adaptation settings when evaluated on automatic speech recognition tasks.

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