22 citations · 28 across the 6 of their papers we have counts for
11 papers
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models
Chenguang Wang, Xiao Liu, Dawn Song
We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, ma…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Linyuan Gong +1
The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike…
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
Chenguang Wang, Xiao Liu, Zui Chen +3
We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize…
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
Chenguang Wang, Ziwen Liu
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a univers…
Language Models are Open Knowledge Graphs
Chenguang Wang, Xiao Liu, Dawn Song
This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are bu…