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Found 47 papers, 12 papers with code
结合标签转移关系的多任务笑点识别方法(Multi-task punchlines recognition method combined with label transfer relationship)
“幽默在人类交流中扮演着重要角色, 并大量存在于情景喜剧中。笑点(punchline)是情景喜剧实现幽默效果的形式之一, 在情景喜剧笑点识别任务中, 每条句子的标签代表该句是否为笑点, 但是以往的笑点识别工作通常只通过建模上下文语义关系识别笑点, 对标签的利用并不充分。为了充分利用标签序列中的信息, 本文提出了一种新的识别方法, 即结合条件随机场的单词级-句子级多任务学习模型, 该模型在两方面进行了改进, 首先将标签序列中相邻两个标签之间的转移关系看作幽默理论中不一致性的一种体现, 并使用条件随机场学习这种转移关系, 其次由于学习相邻标签之间的转移关系以及上下文语义关系均能够学习到铺垫和笑点之间的不一致性, 两者之间存在相关性, 为了使模型通过利用这种相关性提高笑点识别的效果, 该模型引入了多任务学习方法, 使用多任务学习方法同时学习每条句子的句义、组成每条句子的所有字符的词义, 单词级别的标签转移关系以及句子级别的标签转移关系。本文在CCL2020“小牛杯”幽默计算—情景喜剧笑点识别评测任务的英文数据集上进行实验, 结果表明, 本文提出的方法比目前最好的方法提高了3. 2%, 在情景喜剧幽默笑点识别任务上取得了最好的效果, 并通过消融实验证明了上述两方面改进的有效性。”
基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)
幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展, 幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体, 忽视了幽默文本内部的语义关系。本文将幽默等级识别视为自然语言推理任务, 将幽默文本划分为“铺垫”和“笑点”两个部分, 分别对其语义和语义关系进行建模, 提出了一种多粒度语义交互理解网络, 从单词和子句两个粒度捕获幽默文本中语义的关联和交互。本文在Reddit公开幽默数据集上进行了实验, 相比之前最优结果, 模型在语料上的准确率提升了1. 3%。实验表明, 引入幽默内部的语义关系信息可以提高模型幽默识别的性能, 而本文提出的模型也可以很好地建模这种语义关系。
Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification
At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation.
软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
“软件源代码的理解则是软件协同开发与维护的核心, 而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用, 传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上, 提出一种全新的标识符可理解性评价标准。具体而言, 本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程, 即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上, 本文提出一种结合自然语料库的软件标识符规范性评价方法, 用来衡量软件标识符是否易于理解。最后, 本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验, 结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。”
基于风格化嵌入的中文文本风格迁移(Chinese text style transfer based on stylized embedding)
“对话风格能够反映对话者的属性, 例如情感、性别和教育背景等。在对话系统中, 通过理解用户的对话风格, 能够更好地对用户进行建模。同样的, 面对不同背景的用户, 对话机器人也应该使用不同的语言风格与之交流。语言表达风格是文本的内在属性, 然而现有的大多数文本风格迁移研究, 集中在英文领域, 在中文领域则研究较少。本文构建了三个可用于中文文本风格迁移研究的数据集, 并将多种已有的文本风格迁移方法应用于该数据集。同时, 本文提出了基于DeepStyle算法与Transformer的风格迁移模型, 通过预训练可以获得不同风格的隐层向量表示。并基于Transformer构建生成端模型, 在解码阶段, 通过重建源文本的方式, 保留生成文本的内容信息, 并且引入对立风格的嵌入表示, 使得模型能够生成不同风格的文本。实验结果表明, 本文提出的模型在构建的中文数据集上均优于现有模型。”
Locality Preserving Sentence Encoding
Thus, cosine similarity cannot approximate distances on the manifold.
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital.
Leveraging Digital Perceptual Technologies for Remote Perception and Analysis of Human Biomechanical Processes: A Contactless Approach for Workload and Joint Force Assessment
Statistical analyses consistently support the framework's reliability, with joint angle estimations showing less than a 5-degree difference for hip flexion, elbow flexion, and knee angle methods.
Understanding Heterophily for Graph Neural Networks
Firstly, we show that by applying a GC operation, the separability gains are determined by two factors, i. e., the Euclidean distance of the neighborhood distributions and $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree.
Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures.
VR PreM+ : An Immersive Pre-learning Branching Visualization System for Museum Tours
We present VR PreM+, an innovative VR system designed to enhance web exploration beyond traditional computer screens.
Beyond Co-occurrence: Multi-modal Session-based Recommendation
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network
Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
In addition, it is crucial to introduce lexical knowledge to detect the toxicity of posts, which has been a challenge for researchers.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module.
CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction
Recent advances in neural reconstruction using posed image sequences have made remarkable progress.
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses
Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates.
Heterophily-Aware Graph Attention Network
In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i. e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors.
Binary Graph Convolutional Network with Capacity Exploration
The current success of Graph Neural Networks (GNNs) usually relies on loading the entire attributed graph for processing, which may not be satisfied with limited memory resources, especially when the attributed graph is large.
Class-Level Confidence Based 3D Semi-Supervised Learning
Firstly, a dynamic thresholding strategy is proposed to utilize more unlabeled data, especially for low learning status classes.
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
Diverse Message Passing for Attribute with Heterophily
Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge.
Automatic Impact-sounding Acoustic Inspection of Concrete Structure
Impact sounding signal has been shown to contain information about structural integrity flaws and subsurface objects from previous research.
Hate Speech Detection Based on Sentiment Knowledge Sharing
The wanton spread of hate speech on the internet brings great harm to society and families.
MultiMET: A Multimodal Dataset for Metaphor Understanding
Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging.
Fast and Explicit Neural View Synthesis
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar
Ground Penetrating Radar (GPR) is a very useful non-destructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction.
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity
In this paper, the appealing features of a dual-polarized intelligent reflecting surface (IRS) are exploited to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC).
Towards 3D Metric GPR Imaging Based on DNN Noise Removal and Dielectric Estimation
Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i. e., rebars, utility pipes) and reveal the underground scene.
IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization
By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain.
GPR-based Model Reconstruction System for Underground Utilities Using GPRNet
In this paper, both the quantitative and qualitative experiment results verify our method that can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i. e., GPR raw data incompleteness and various noise.
Bi-GCN: Binary Graph Convolutional Network
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning.
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet
This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i. e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets.
Secrecy Outage Probability Analysis for RIS-Assisted NOMA Systems
In this paper, the physical layer security (PLS) for a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system in a multi-user scenario is investigated, where we consider the worst case that the eavesdropper also utilizes the advantage of the RISs.
ALBERT-BiLSTM for Sequential Metaphor Detection
In our daily life, metaphor is a common way of expression.
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects.
A Refined Margin Distribution Analysis for Forest Representation Learning
In this paper, we formulate the forest representation learning approach called \textsc{CasDF} as an additive model which boosts the augmented feature instead of the prediction.
Multi-Label Learning with Deep Forest
In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models.
Forest Representation Learning Guided by Margin Distribution
In this paper, we reformulate the forest representation learning approach as an additive model which boosts the augmented feature instead of the prediction.
Ego-Downward and Ambient Video based Person Location Association
To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns.
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions.
