no code implementations • EMNLP 2020 • Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, Weijian Sun
In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.
no code implementations • EMNLP 2020 • Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, Zhicheng Sheng
Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.
2 code implementations • 21 Jun 2024 • Wentong Chen, Yankai Lin, ZhenHao Zhou, HongYun Huang, Yantao Jia, Zhao Cao, Ji-Rong Wen
In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs.
1 code implementation • 8 Aug 2022 • Chenwei Ran, Wei Shen, Jianbo Gao, Yuhan Li, Jianyong Wang, Yantao Jia
Entity linking (EL) is the process of linking entity mentions appearing in text with their corresponding entities in a knowledge base.
no code implementations • 28 Feb 2022 • Daniel Gao, Yantao Jia, Lei LI, Chengzhen Fu, Zhicheng Dou, Hao Jiang, Xinyu Zhang, Lei Chen, Zhao Cao
However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs.
no code implementations • 14 Oct 2021 • Hao Jiang, Ke Zhan, Jianwei Qu, Yongkang Wu, Zhaoye Fei, Xinyu Zhang, Lei Chen, Zhicheng Dou, Xipeng Qiu, Zikai Guo, Ruofei Lai, Jiawen Wu, Enrui Hu, Yinxia Zhang, Yantao Jia, Fan Yu, Zhao Cao
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
no code implementations • 14 Sep 2021 • Ruizhi Pu, Xinyu Zhang, Ruofei Lai, Zikai Guo, Yinxia Zhang, Hao Jiang, Yongkang Wu, Yantao Jia, Zhicheng Dou, Zhao Cao
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.
3 code implementations • Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021 • Xinyu Zhang, Ke Zhan, Enrui Hu, Chengzhen Fu, Lan Luo, Hao Jiang, Yantao Jia, Fan Yu, Zhicheng Dou, Zhao Cao, Lei Chen
Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.
no code implementations • 18 May 2021 • Hao Jiang, Yutao Zhu, Xinyu Zhang, Zhicheng Dou, Pan Du, Te Pi, Yantao Jia
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
1 code implementation • 15 Feb 2021 • Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen
The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.
no code implementations • 1 Jan 2021 • Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Yantao Jia, Zonggang Yuan, Huajun Chen
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-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Jian Liu, Yubo Chen, Kang Liu, Yantao Jia, Zhicheng Sheng
Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE).
2 code implementations • 24 Oct 2020 • Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, Huajun Chen
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
1 code implementation • 15 Sep 2020 • Haiyang Yu, Ningyu Zhang, Shumin Deng, Zonggang Yuan, Yantao Jia, Huajun Chen
Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance.
no code implementations • 7 Apr 2020 • Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen
However, the side information of classes used now is limited to text descriptions and attribute annotations, which are in short of semantics of the classes.
no code implementations • 23 Aug 2019 • Zhepei Wei, Yantao Jia, Yuan Tian, Mohammad Javad Hosseini, Sujian Li, Mark Steedman, Yi Chang
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
1 code implementation • EMNLP 2018 • Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.
no code implementations • 29 Oct 2017 • Denghui Zhang, Pengshan Cai, Yantao Jia, Manling Li, Yuanzhuo Wang, Xue-Qi Cheng
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.
1 code implementation • 30 Mar 2017 • Denghui Zhang, Manling Li, Yantao Jia, Yuanzhuo Wang, Xue-Qi Cheng
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.
Ranked #1 on
Link Prediction
on WN18 (filtered)
no code implementations • 4 Dec 2015 • Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, Xue-Qi Cheng
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.