22 citations · 50 across the 5 of their papers we have counts for
8 papers
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models
Deming Ye, Yankai Lin, Peng Li +2
Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose…
TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference
Deming Ye, Yankai Lin, Yufei Huang +1
Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To addres…
CPM: A Large-scale Generative Chinese Pre-trained Language Model
Zhengyan Zhang, Xu Han, Hao Zhou +22
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot o…
Coreferential Reasoning Learning for Language Representation
Deming Ye, Yankai Lin, Jiaju Du +4
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of…
Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network
Deming Ye, Yankai Lin, Zhenghao Liu +2
Multi-paragraph reasoning is indispensable for open-domain question answering (OpenQA), which receives less attention in the current OpenQA systems. In this work, we propose a know…
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
Xu Han, Tianyu Gao, Yuan Yao +3
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE…