no code implementations • CCL 2021 • Shuning Lv, Jian Liu, Jinan Xu, Yufeng Chen, Yujie Zhang
“实体边界预测对中文命名实体识别至关重要。现有研究为改善边界识别效果提出的多任务学习方法仅考虑与分词任务结合, 缺少多任务标签训练数据, 无法学到任务的标签一致性关系。本文提出一种新的基于多任务标签一致性机制的中文命名实体识别方法:将分词和词性信息融入命名实体识别模型, 使三种任务联合训练;建立基于标签一致性机制的多任务学习模式, 来捕获标签一致性关系及学习多任务表示。全样本和小样本实验表明了方法的有效性。”
Chinese Named Entity Recognition named-entity-recognition +1
no code implementations • CCL 2021 • Bo Jin, Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen
“如何挖掘语言资源中丰富的复述模板, 是复述研究中的一项重要任务。已有方法在人工给定种子实体对的基础上, 利用实体关系, 通过自举迭代方式, 从开放域获取复述模板, 规避对平行语料或可比语料的依赖, 但是该方法需人工给定实体对, 实体关系受限;在迭代过程中语义会发生偏移, 影响获取质量。针对这些问题, 我们考虑知识库中包含描述特定语义关系的实体对(即关系三元组), 提出融合外部知识的开放域复述模板自动获取方法。首先, 将关系三元组与开放域文本对齐, 获取关系对应文本, 并将文本中语义丰富部分泛化成变量槽, 获取关系模板;接着设计模板表示方法, 本文利用预训练语言模型, 在模板表示中融合变量槽语义;最后, 根据获得的模板表示, 设计自动聚类与筛选方法, 获取高精度的复述模板。在融合自动评测与人工评测的评价方法下, 实验结果表明, 本文提出的方法实现了在开放域数据上复述模板的自动泛化与获取, 能够获得质量高、语义一致的复述模板。”
1 code implementation • EMNLP 2021 • Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen
Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.
no code implementations • Findings (NAACL) 2022 • Erguang Yang, Chenglin Bai, Deyi Xiong, Yujie Zhang, Yao Meng, Jinan Xu, Yufeng Chen
To model the alignment relation between words and nodes, we propose an attention regularization objective, which makes the decoder accurately select corresponding syntax nodes to guide the generation of words. Experiments show that SI-SCP achieves state-of-the-art performances in terms of semantic and syntactic quality on two popular benchmark datasets. Additionally, we propose a Syntactic Template Retriever (STR) to retrieve compatible syntactic structures.
no code implementations • CCL 2020 • Xingchen Li, Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen
The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0. 38% on dependency parsing than the model of Yan et al. (2019).
no code implementations • 5 Jun 2024 • Junhui Rao, Yujie Zhang, Shiwen Tang, Zan Li, Zhaoyang Ming, Jichen Zhang, Chi Yuk Chiu, Ross Murch
This design aims to bridge the gap between current single-band reconfigurable intelligent surfaces (RISs) and wireless systems utilizing sub-6 GHz and mmWave bands that require RIS with independently reconfigurable dual-band operation.
no code implementations • 10 May 2024 • Yujie Zhang, Neil Gong, Michael K. Reiter
Federated Learning (FL) is a decentralized machine learning method that enables participants to collaboratively train a model without sharing their private data.
no code implementations • 15 Mar 2024 • Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Jenq-Neng Hwang, Xiaozhong Xu, Shan Liu
Furthermore, in the model fine-tuning stage, we propose a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives.
no code implementations • 15 Mar 2024 • Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Shan Liu
Furthermore, in the model fine-tuning stage, the learned content-aware features serve as a guide to fuse the point cloud quality features extracted from different perspectives.
no code implementations • 4 Jul 2023 • Yipeng Liu, Qi Yang, Yujie Zhang, Yiling Xu, Le Yang, Xiaozhong Xu, Shan Liu
Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from subjective experiments, by considering the domain relevance among samples located in the perception domain and learning a structured latent space.
2 code implementations • 28 Jun 2023 • Yining Hua, Jiageng Wu, Shixu Lin, Minghui Li, Yujie Zhang, Dinah Foer, Siwen Wang, Peilin Zhou, Jie Yang, Li Zhou
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
1 code implementation • 19 Apr 2023 • Yujie Zhang, Justin Chu, Haoyu Cheng, Heng Li
Existing algorithms for identifying satellite repeats either require the complete assembly of satellites or only work for simple repeat structures without HORs.
no code implementations • 29 Oct 2022 • Ziyu Shan, Qi Yang, Rui Ye, Yujie Zhang, Yiling Xu, Xiaozhong Xu, Shan Liu
To extract effective features for PCQA, we propose a new graph convolution kernel, i. e., GPAConv, which attentively captures the perturbation of structure and texture.
1 code implementation • 10 Oct 2022 • Yujie Zhang, Qi Yang, Yifei Zhou, Xiaozhong Xu, Le Yang, Yiling Xu
The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner.
no code implementations • 30 Sep 2022 • Zhengyu Wang, Yujie Zhang, Qi Yang, Yiling Xu, Jun Sun, Shan Liu
Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using 3D saliency maps (PQSM).
no code implementations • 25 May 2022 • Zixiang Han, Shanpu Shen, Yujie Zhang, Shiwen Tang, Chi-Yuk Chiu, Ross Murch
Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided.
no code implementations • 7 Feb 2022 • Antony Orth, Kathleen L. Sampson, Yujie Zhang, Kayley Ting, Derek Aranguren van Egmond, Kurtis Laqua, Thomas Lacelle, Daniel Webber, Dorothy Fathi, Jonathan Boisvert, Chantal Paquet
Additive manufacturing techniques are revolutionizing product development by enabling fast turnaround from design to fabrication.
no code implementations • 26 Nov 2021 • Yujie Zhang
In a recorded situation, textual information is crucial for scene interpretation and decision making.
1 code implementation • 4 Mar 2021 • Qi Yang, Yujie Zhang, Siheng Chen, Yiling Xu, Jun Sun, Zhan Ma
In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).
no code implementations • COLING 2020 • Mingtong Liu, Erguang Yang, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen
We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.