Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training

• URL: http://arxiv.org/abs/2212.10503v2
• Date: Tue, 4 Jul 2023 19:06:55 GMT
• Title: Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training
• Authors: Kelly Marchisio, Patrick Lewis, Yihong Chen, Mikel Artetxe
• Abstract summary: It is possible to expand pretrained Masked Language Models to new languages by learning a new set of embeddings, while keeping the transformer body frozen. We propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model and plugged into the aligned large model for rapid cross-lingual transfer.
• Score: 36.5936227129021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full forward and backward pass over the entire model. We propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model and plugged into the aligned large model for rapid cross-lingual transfer. We explore two approaches to learn mini-models: MiniJoint, which jointly pretrains the primary model and the mini-model using a single transformer with a secondary MLM head at a middle layer; and MiniPost, where we start from a regular pretrained model, build a mini-model by extracting and freezing a few layers, and learn a small number of parameters on top. Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the performance of the standard approach using 2.3x less compute on average.

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