XtremeDistil: Multi-stage Distillation for Massive Multilingual Models
This work addresses the challenge of deploying resource-intensive multilingual models in real-world applications, offering a significant compression technique for NER tasks.
The paper tackles the problem of compressing large multilingual pre-trained language models for practical use by proposing a multi-stage distillation method for Named Entity Recognition (NER), achieving up to 35x parameter compression and 51x latency reduction while retaining 95% F1-score across 41 languages.
Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks. However, the huge size of these models could be a deterrent to use them in practice. Some recent and concurrent works use knowledge distillation to compress these huge models into shallow ones. In this work we study knowledge distillation with a focus on multi-lingual Named Entity Recognition (NER). In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations that is agnostic of teacher architecture and show that it outperforms strategies employed in prior works. Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few. We show that our approach leads to massive compression of MBERT-like teacher models by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95% of its F1-score for NER over 41 languages.