42 citations · 111 across the 11 of their papers we have counts for
11 papers
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models
Deming Ye, Yankai Lin, Zhengyan Zhang +1
Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge inje…
Plug-and-Play Knowledge Injection for Pre-trained Language Models
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +8
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy ne…
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…
Packed Levitated Marker for Entity and Relation Extraction
Deming Ye, Yankai Lin, Peng Li +1
Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing…
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…
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yuan Yao, Deming Ye, Peng Li +7
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on…
Rethinking the Form of Latent States in Image Captioning
Bo Dai, Deming Ye, Dahua Lin
RNNs and their variants have been widely adopted for image captioning. In RNNs, the production of a caption is driven by a sequence of latent states. Existing captioning models usu…