no code implementations • 16 Aug 2024 • Lei Hei, Ning An, Tingjing Liao, Qi Ma, Jiaqi Wang, Feiliang Ren
Multimodal Relation Extraction is crucial for constructing flexible and realistic knowledge graphs.
no code implementations • 29 Apr 2024 • Ning An, Lei Hei, Yong Jiang, Weiping Meng, Jingjing Hu, Boran Huang, Feiliang Ren
Relational triple extraction is crucial work for the automatic construction of knowledge graphs.
1 code implementation • 9 Jan 2024 • Jiaqi Wang, Yuying Chang, Zhong Li, Ning An, Qi Ma, Lei Hei, Haibo Luo, Yifei Lu, Feiliang Ren
Large language models have exhibited robust performance across diverse natural language processing tasks.
1 code implementation • 1 Jul 2022 • Feiliang Ren, Yongkang Liu, Bochao Li, Shilei Liu, Bingchao Wang, Jiaqi Wang, Chunchao Liu, Qi Ma
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
no code implementations • 25 Feb 2022 • Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
1 code implementation • 9 Dec 2021 • Feiliang Ren, Longhui Zhang, Xiaofeng Zhao, Shujuan Yin, Shilei Liu, Bochao Li
Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods.
1 code implementation • EMNLP 2021 • Feiliang Ren, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Shilei Liu, Bochao Li, Yaduo Liu
Next, the mined global associations are integrated into the table feature of each relation.
1 code implementation • EMNLP 2021 • Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren, Longhui Zhang, Shujuan Yin
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
no code implementations • 31 Aug 2021 • Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research interest.
1 code implementation • 20 Aug 2021 • Feiliang Ren, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Shilei Liu, Bochao Li
Tagging based methods are one of the mainstream methods in relational triple extraction.
1 code implementation • 16 Aug 2021 • Yaduo Liu, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Feiliang Ren
Finally, our system ranks No. 4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79. 887%, 85. 179%, and 70. 828% respectively.
no code implementations • 21 Dec 2020 • Yongkang Liu, Shi Feng, Daling Wang, Kaisong Song, Feiliang Ren, Yifei Zhang
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
no code implementations • SEMEVAL 2020 • Huihui Zhang, Feiliang Ren
The paper describes our system BERTatDE1 in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6).
no code implementations • COLING 2020 • Feiliang Ren, Juchen Li, Huihui Zhang, Shilei Liu, Bochao Li, Ruicheng Ming, Yujia Bai
To address this issue, we propose a simple but effective atrous convolution based knowledge graph embedding method.
Ranked #1 on
Knowledge Graph Embedding
on FB15k
no code implementations • SEMEVAL 2020 • Shilei Liu, Yu Guo, Bochao Li, Feiliang Ren
This paper describes our submission to subtask a and b of SemEval-2020 Task 4.
no code implementations • 26 Mar 2019 • Cunxiang Wang, Feiliang Ren, Zhichao Lin, Chenxv Zhao, Tian Xie, Yue Zhang
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.
no code implementations • 17 Dec 2018 • Feiliang Ren, Yining Hou, Yan Li, Linfeng Pan, Yi Zhang, Xiaobo Liang, Yongkang Liu, Yu Guo, Rongsheng Zhao, Ruicheng Ming, Huiming Wu
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
no code implementations • COLING 2018 • Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang
State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given.