11 citations · 26 across the 10 of their papers we have counts for
10 papers
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Dan Qiao, Yi Su, Pinzheng Wang +18
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
Dan Qiao, Chenchen Dai, Yuyang Ding +4
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downst…
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
Junjie Yu, Xing Wang, Jiangjiang Zhao +2
We present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction. The approach first classifies the auto-annotated instances into two g…
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Tong Zhu, Xiaoye Qu, Wenliang Chen +4
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in b…
Exploiting Rich Syntax for Better Knowledge Base Question Answering
Pengju Zhang, Yonghui Jia, Muhua Zhu +2
Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly fo…
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
Tong Zhu, Haitao Wang, Junjie Yu +4
In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-sc…
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Xianpei Han, Zhichun Wang, Jiangtao Zhang +22
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation…
Improving Neural Relation Extraction with Positive and Unlabeled Learning
Zhengqiu He, Wenliang Chen, Yuyi Wang +3
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforceme…
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
Haitao Wang, Zhengqiu He, Tong Zhu +3
The CCKS2019 shared task was devoted to inter-personal relationship extraction. Given two person entities and at least one sentence containing these two entities, participating tea…
IPRE: a Dataset for Inter-Personal Relationship Extraction
Haitao Wang, Zhengqiu He, Jin Ma +2
Inter-personal relationship is the basis of human society. In order to automatically identify the relations between persons from texts, we need annotated data for training systems.…