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Found 28 papers, 5 papers with code
“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)
机器翻译错误分析旨在找出机器译文中存在的错误, 包括错误类型、错误分布等, 它在机器翻译研究和应用中起着重要作用。该文将人工译后编辑与错误分析结合起来, 对译后编辑操作进行错误标注, 采用自动标注和人工标注相结合的方法, 构建了一个细粒度英汉机器翻译错误分析语料库, 其中每一个标注样本包括源语言句子、机器译文、人工参考译文、译后编辑译文、词错误率和错误类型标注;标注的错误类型包括增词、漏词、错词、词序错误、未译和命名实体翻译错误等。标注的一致性检验表明了标注的有效性;对标注语料的统计分析结果能有效地指导机器翻译系统的开发和人工译员的后编辑。
Synergizing Knowledge Graphs with Large Language Models: A Comprehensive Review and Future Prospects
Recent advancements have witnessed the ascension of Large Language Models (LLMs), endowed with prodigious linguistic capabilities, albeit marred by shortcomings including factual inconsistencies and opacity.
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs.
SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs
Building natural language interfaces typically uses a semantic parser to parse the user's natural language and convert it into structured \textbf{S}emantic \textbf{L}ogic \textbf{F}orms (SLFs).
Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance.
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively.
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority.
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem.
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
De novo peptide sequencing is a promising approach for novel peptide discovery.
Exploring Global and Local Information for Anomaly Detection with Normal Samples
To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples.
PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction
In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams.
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity
This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links.
FMGNN: Fused Manifold Graph Neural Network
Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks.
TopoSeg: Topology-Aware Nuclear Instance Segmentation
Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance.
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
CC-Riddle: A Question Answering Dataset of Chinese Character Riddles
Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language.
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters.
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks
Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals.
Periodic repeating fast radio bursts: interaction between a magnetized neutron star and its planet in an eccentric orbit
Fast radio bursts (FRBs) are mysterious transient phenomena.
High Energy Astrophysical Phenomena
X-ray Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology
After correcting for the redshift evolution, we show that the de-evolved $L-T-E_{\rm{p}}$ correlation can be used as a standard candle.
High Energy Astrophysical Phenomena
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.
An entity-driven recursive neural network model for chinese discourse coherence modeling
Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing field. Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated features to capture the logic or syntactic or semantic relationships acrosssentences within a text. In this paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation based on current English discourse coherenceneural network model.
Sentence-level dialects identification in the greater China region
Identifying the different varieties of the same language is more challenging than unrelated languages identification.
