7 citations · 18 across the 4 of their papers we have counts for
8 papers
Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation
Cheng Chen, Yichun Yin, Lifeng Shang +4
Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression. Although achieving promising results on NLP tasks, it requires en…
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
Xiaoqi Jiao, Yichun Yin, Lifeng Shang +5
The multilingual pre-trained language models (e.g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks. However, these mod…
Improving Task-Agnostic BERT Distillation with Layer Mapping Search
Xiaoqi Jiao, Huating Chang, Yichun Yin +6
Knowledge distillation (KD) which transfers the knowledge from a large teacher model to a small student model, has been widely used to compress the BERT model recently. Besides the…
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Wei Zhang, Lu Hou, Yichun Yin +4
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks.However, these models are both computation and memory…
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation
Yun Chen, Liangyou Li, Xin Jiang +2
Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observ…
TinyBERT: Distilling BERT for Natural Language Understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang +5
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually c…