Is Modularity Transferable? A Case Study through the Lens of Knowledge
  Distillation
Mateusz Klimaszewski Piotr Andruszkiewicz Alexandra Birch
The rise of Modular Deep Learning showcases its potential in various Natural
Language Processing applications. Parameter-efficient fine-tuning (PEFT)
modularity has been shown to work for various use cases, from domain adaptation
to multilingual setups. However, all this work covers the case where the
modular components are trained and deployed within one single Pre-trained
Language Model (PLM). This model-specific setup is a substantial limitation on
the very modularity that modular architectures are trying to achieve. We ask
whether current modular approaches are transferable between models and whether
we can transfer the modules from more robust and larger PLMs to smaller ones.
In this work, we aim to fill this gap via a lens of Knowledge Distillation,
commonly used for model compression, and present an extremely straightforward
approach to transferring pre-trained, task-specific PEFT modules between
same-family PLMs. Moreover, we propose a method that allows the transfer of
modules between incompatible PLMs without any change in the inference
complexity. The experiments on Named Entity Recognition, Natural Language
Inference, and Paraphrase Identification tasks over multiple languages and PEFT
methods showcase the initial potential of transferable modularity.