134 citations · 264 across the 14 of their papers we have counts for
14 papers
bert2BERT: Towards Reusable Pretrained Language Models
Cheng Chen, Yichun Yin, Lifeng Shang +7
In recent years, researchers tend to pre-train ever-larger language models to explore the upper limit of deep models. However, large language model pre-training costs intensive com…
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Baojun Wang, Zhao Zhang, Kun Xu +7
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing…
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
Yichun Yin, Cheng Chen, Lifeng Shang +3
Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the h…
Automatic Construction of Sememe Knowledge Bases via Dictionaries
Fanchao Qi, Yangyi Chen, Fengyu Wang +3
A sememe is defined as the minimum semantic unit in linguistics. Sememe knowledge bases (SKBs), which comprise words annotated with sememes, enable sememes to be applied to natural…
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…