most citedDocument-level Relation Extraction as Semantic Segmentation

12 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2021★ 12 cited

Document-level Relation Extraction as Semantic Segmentation

Ningyu Zhang, Xiang Chen, Xin Xie +6

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the en…

cs.AI2021★ 5 cited

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Ningyu Zhang, Qianghuai Jia, Shumin Deng +8

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-…

cs.CL2021★ 2 cited

Disentangled Contrastive Learning for Learning Robust Textual Representations

Xiang Chen, Xin Xie, Zhen Bi +4

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-o…

cs.CL2021★ 2 cited

Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction

Luoqiu Li, Xiang Chen, Zhen Bi +6

Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thu…

cs.CL2021★ 2 cited

ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

Xin Xie, Xiangnan Chen, Xiang Chen +4

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and t…