2 code implementations • arXiv 2019 • Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang
For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.
no code implementations • 31 Oct 2019 • Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network.
3 code implementations • ACL 2020 • Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Haotang Deng, Qi Ju
Pre-trained language models like BERT have proven to be highly performant.
no code implementations • 31 Aug 2020 • Xukun Luo, Weijie Liu, Meng Ma, Ping Wang
In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence.
Ranked #1 on Relation Extraction on DuIE
no code implementations • COLING 2020 • Chen Lyu, Weijie Liu, Ping Wang
In this paper, we propose a new few-shot text classification method.
no code implementations • 2 Dec 2020 • Weijie Liu, Chao Zhang, Jiahao Xie, Zebang Shen, Hui Qian, Nenggan Zheng
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
3 code implementations • 29 Mar 2021 • Jianlin Su, Jiarun Cao, Weijie Liu, Yangyiwen Ou
Therefore, some attempts of boosting the isotropy of sentence distribution, such as flow-based model, have been applied to sentence representations and achieved some improvement.
1 code implementation • 29 May 2021 • Jiahao Xie, Chao Zhang, Zebang Shen, Weijie Liu, Hui Qian
We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks.
1 code implementation • 4 Aug 2021 • Weijie Liu, Chong Wang, Haohe Li, Shenghao Yu, Jiafei Wu
By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly.
no code implementations • 12 Nov 2021 • Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.
no code implementations • 6 Feb 2022 • Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian
In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.
1 code implementation • 14 Feb 2022 • Weijie Liu, Tao Zhu, Weiquan Mao, Zhe Zhao, Weigang Guo, Xuefeng Yang, Qi Ju
In this paper, we pay attention to the issue which is usually overlooked, i. e., \textit{similarity should be determined from different perspectives}.
1 code implementation • 10 Jun 2022 • Zhiquan Lai, Shengwei Li, Xudong Tang, Keshi Ge, Weijie Liu, Yabo Duan, Linbo Qiao, Dongsheng Li
These features make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism and tensor model parallelism, to achieve high training efficiency.
1 code implementation • COLING 2022 • Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, HUI ZHANG
The CSL can serve as a Chinese corpus.
1 code implementation • Findings (NAACL) 2022 • Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.
no code implementations • 19 Sep 2022 • Rong Tian, Zijing Zhao, Weijie Liu, Haoyan Liu, Weiquan Mao, Zhe Zhao, Kan Zhou
The latest industrial inference engines, such as FasterTransformer and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed.
1 code implementation • COLING 2022 • Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages.
1 code implementation • 20 Nov 2022 • Taiqiang Wu, Xingyu Bai, Weigang Guo, Weijie Liu, Siheng Li, Yujiu Yang
We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph.
3 code implementations • 13 Dec 2022 • Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Guo, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
no code implementations • 19 May 2023 • Xingyu Bai, Taiqiang Wu, Han Guo, Zhe Zhao, Xuefeng Yang, Jiayi Li, Weijie Liu, Qi Ju, Weigang Guo, Yujiu Yang
Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs).
no code implementations • 29 Jun 2023 • Jiahao Xie, Chao Zhang, Weijie Liu, Wensong Bai, Hui Qian
The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications.
no code implementations • 20 Oct 2023 • Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong, Zhi Zhou, Xu Chen
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.
no code implementations • NAACL (ACL) 2022 • Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang
Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.