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<h2 id="论文信息"><a href="#论文信息" class="headerlink" title="论文信息"></a>论文信息</h2><p>论文下载：<a target="_blank" rel="noopener" href="https://ojs.aaai.org/index.php/AAAI/article/view/26168">Neighbor Contrastive Learning on Learnable Graph Augmentation</a><br>标题：基于可学习图增强的邻居监督图对比学习<br>作者：Xiao Shen（海南大学）, Dewang Sun, Shirui Pan, Xi Zhou, Laurence T. Yang<br>发表地点：AAAI 2023<br>代码开源：<a target="_blank" rel="noopener" href="https://github.com/shenxiaocam/NCLA">https://github.com/shenxiaocam/NCLA</a></p>
<h2 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h2><p>近年来，旨在从未标记的图中学习表示的图对比学习（GCL）取得了长足的进步。然而，现有的GCL方法大多采用人工设计的图增强，对各种图数据集都很敏感。此外，最初在计算机视觉中产生的对比损失已直接应用于图形数据，其中相邻节点被视为负值，因此与锚点相距很远。然而，这与网络的同质假设相矛盾，即连接的节点通常属于同一类，并且应该彼此靠近。在这项工作中，我们提出了一种名为NCLA的端到端自动GCL方法，用于将邻居对比学习应用于可学习图增强。多头图注意力机制自动学习多个自适应拓扑图增强视图，无需先验领域知识即可兼容各种图数据集。此外，通过将网络拓扑作为监督信号，设计了邻域对比损失，以允许每个锚点具有多个正信号。在拟议的 NCLA 中，增强和嵌入都是端到端学习的。在基准数据集上的大量实验表明，当标签极其有限时，NCLA在自监督GCL上产生了最先进的节点分类性能，甚至超过了监督节点。我们的代码在 <a target="_blank" rel="noopener" href="https://github.com/shenxiaocam/NCLA">https://github.com/shenxiaocam/NCLA</a> 上发布。</p>
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<p>2019-2023年来，<strong>多模态嘲讽检测（Multi-Modal Sarcasm Detection，MSD）</strong>任务收到广泛关注，出现了一系列优秀的模型。<br>HFM 设计了一个分层融合模型来组合来自两种模态的信息，并提出了一个有公信力、较公平权威的基于 Twitter 的多模态讽刺检测数据集，为后续对 MSD 的研究提供了基础。D&R Net 使用语义关联上下文来查找讽刺线索。Att-BERT 通过自注意力机制融合视觉和文本嵌入。在 InCrossMGs 引入了一个图网络来描述图像文本对。CMGCN 通过跨模态图卷积网络构建区域和单词之间的连接。HKE 挖掘外部知识以构建原子级一致性和组合级一致性分层框架。MILNet 利用 OCR 辅助图像检测，提出相互增强不协调学习网络。DIP 提出双重感知网络，从事实和情感层面学习讽刺信息。</p>
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<p>这里主要是整理自己在研究多模态嘲讽检测邻域时阅读到的比较好有借鉴意义的论文。<br>【论文阅读分类】</p>
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<h2 id="论文信息"><a href="#论文信息" class="headerlink" title="论文信息"></a>论文信息</h2><p>论文下载：<a target="_blank" rel="noopener" href="https://arxiv.org/abs/2202.02516">A SURVEY ON AUTOMATED SARCASM DETECTION ON TWITTER</a><br>标题：基于 Twitter 的嘲讽检测论文调查<br>作者：Bleau Moores, Vijay Mago<br>发表地点：A PREPRINT in Arxiv（预印版）</p>
<h2 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h2><p>自动嘲讽检测是计算机科学中一个不断发展的领域。短文信息越来越多地被用于交流，尤其是在 Twitter 等社交媒体平台上。由于语境不足或缺失，这些信息中未识别的嘲讽会颠倒语句的含义，导致混乱和交流失败。本文介绍了当前用于嘲讽检测的各种方法，包括通过上下文、发帖历史和机器学习模型进行检测。此外，还可以观察到向深度学习方法的转变，这可能是由于使用具有诱导特征而非离散特征的模型与变压器的创新相结合所带来的好处。</p>
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<h2 id="论文信息"><a href="#论文信息" class="headerlink" title="论文信息"></a>论文信息</h2><p>论文下载：<a target="_blank" rel="noopener" href="https://www.mdpi.com/2227-7390/10/24/4706">Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection</a><br>标题：关键词增强的多专家框架用于仇恨言论检测<br>作者：钟玮瑜、吴乔峰（共一）、卢国钧、薛云、胡晓辉（通讯）<br>发表地点：Mathematics 2022, 10(24), 4706</p>
<h2 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h2><p>由于仇恨言论在互联网上的传播，会给人的身心健康和社会带来极大的危害，因此建立和促进仇恨言论检测的发展并采取一定的回避机制变得非常迫切。目前现有的仇恨言论检测方法，容易忽略目标句子的情感特征，难以识别出一些隐式的仇恨言论。然而，从其他情感资源中获取更多的情感信息将显著影响仇恨言论检测的性能。在利用外部情感信息时，也不能忽略句子本身的关键信息。于是本文提出了一个基于句子本身关键信息和外部情感信息的仇恨言论检测框架。最后在三个公共数据集上的实验结果证明了我们模型的有效性。</p>
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