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Found 34 papers, 10 papers with code
基于多任务标签一致性机制的中文命名实体识别(Chinese Named Entity Recognition based on Multi-task Label Consistency Mechanism)
“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
融合外部知识的开放域复述模板获取方法(An Open Domain Paraphrasing Template Acquisition Method Based on External Knowledge)
“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。”
Learning Structural Information for Syntax-Controlled Paraphrase Generation
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.
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data
Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.
A Joint Model for Graph-based Chinese Dependency Parsing
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).
LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation
To address these challenges, we introduce LeCoDe, a real-world multi-turn benchmark dataset comprising 3, 696 legal consultation dialogues with 110, 008 dialogue turns, designed to evaluate and improve LLMs' legal consultation capability.
DPCD: A Quality Assessment Database for Dynamic Point Clouds
Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC).
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation.
Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning
Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods.
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior knowledge in images, called the Distribution-Weighted Image-Transferred Point Cloud Quality Assessment (DWIT-PCQA).
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment
In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress.
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-constrained devices.
Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation
Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions.
Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization
However, current NR-PCQA models attempt to indiscriminately learn point cloud content and distortion representations within a single network, overlooking their distinct contributions to quality information.
Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task
Our method utilizes the downstream task of ship target semantic segmentation to guide the training of image translation network, improving the quality of output Optical-styled images.
Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment
Recent years have witnessed the success of the deep learning-based technique in research of no-reference point cloud quality assessment (NR-PCQA).
Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy
To bridge the gap, in this paper, we propose a perception-guided hybrid metric (PHM) that adaptively leverages two visual strategies with respect to distortion degree to predict point cloud quality: to measure visible difference in high-quality samples, PHM takes into account the masking effect and employs texture complexity as an effective compensatory factor for absolute difference; on the other hand, PHM leverages spectral graph theory to evaluate appearance degradation in low-quality samples.
A Shared-Aperture Dual-Band sub-6 GHz and mmWave Reconfigurable Intelligent Surface With Independent Operation
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.
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
To effectively conceal malicious model updates among benign ones, we propose DPOT, a backdoor attack strategy in FL that dynamically constructs backdoor objectives by optimizing a backdoor trigger, making backdoor data have minimal effect on model updates.
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
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.
PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
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.
Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description
Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from the human visual system (HVS), by considering the domain relevance among samples located in the perception domain and learning a structured latent space.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
De novo reconstruction of satellite repeat units from sequence data
Existing algorithms for identifying satellite repeats either require the complete assembly of satellites or only work for simple repeat structures without HORs.
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
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.
TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment
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.
Point Cloud Quality Assessment using 3D Saliency Maps
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).
Using Loaded N-port Structures to Achieve the Continuous-Space Electromagnetic Channel Capacity Bound
Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided.
On-the-fly 3D metrology of volumetric additive manufacturing
Additive manufacturing techniques are revolutionizing product development by enabling fast turnaround from design to fabrication.
Natural Scene Text Editing Based on AI
In a recorded situation, textual information is crucial for scene interpretation and decision making.
MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy
In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning
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.
