Papers with MobileBERT
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation (2022.naacl-main)
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| Challenge: | a recent study shows that over-parameterized pre-trained language models are unsuitable for low-capacity devices. |
| Approach: | They propose a transformer-based pre-trained language model that is overparameterized . they use a two-stage knowledge distillation scheme to train the model . |
| Outcome: | The proposed model outperforms state-of-the-art models on well-known NLP benchmarks. |
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020.acl-main)
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| Challenge: | Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE . BERT is one of the largest models ever in NLP, but suffers from heavy model size and high latency . |
| Approach: | They propose a tool to compress and accelerate the popular BERT model by task-agnostic application. |
| Outcome: | The proposed model is 4.3x smaller and 5.5x faster than BERT_BASE . it achieves competitive results on well-known benchmarks . |
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models (2021.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have achieved great success in natural language processing. |
| Approach: | They propose a method that automatically searches architecture hyper-parameters in BERT . they use one-shot learning and the search space to provide an adaptive development way . |
| Outcome: | The proposed method outperforms both the baseline and distillation-based methods on GLUE and SQUAD benchmarks. |
Token and Head Adaptive Transformers for Efficient Natural Language Processing (2022.coling-1)
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| Challenge: | Pre-trained language models like BERT have shown significant accuracy improvements on various tasks, but their computational cost and memory footprint are prohibitive. |
| Approach: | They propose to extend Length Adaptive Transformer to extend the model to a token and head pruning scheme to optimize pruning efficiency. |
| Outcome: | The proposed model can compress and accelerate BERT-based models by fine-tuning and a token and head pruning scheme. |