Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.

Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing works have to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on all metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.

强有力的对手例子是评估和增强深神经网络鲁棒性的关键。流行的对抗性攻击算法使用梯度上升最大化非cave损失函数。但是，每种攻击的性能通常对由于信息不足（仅一个输入示例，几乎没有白色盒子源模型和未知的防御策略）而敏感。因此，精心设计的对抗性示例容易过度拟合源模型，从而将其转移性限制在身份不明的架构上。在本文中，我们提出了多种渐近正态分布攻击（Multianda），这是一种新颖的方法，可以明确表征来自学习分布的对抗性扰动。具体而言，我们通过利用随机梯度上升（SGA）的渐近正态性能（SGA）的优势来近似于扰动，然后将整体策略应用于此过程，以估算高斯混合模型，以更好地探索潜在的优化空间。从学习分布中绘制扰动使我们能够为每个输入生成任何数量的对抗示例。近似后验实质上描述了SGA迭代的固定分布，该分布捕获了局部最佳距离周围的几何信息。因此，从分布中得出的样品可靠地保持转移性。我们提出的方法通过对七个正常训练和七个防御模型进行广泛的实验，超过了对具有或没有防御的深度学习模型的九个最先进的黑盒攻击。

以一致的性格赋予聊天机器人对于代理商提供类似人类互动的作用至关重要。但是，现有的个性化方法通常会根据用文本描述描绘的静态预定义角色产生响应，这可能严重限制了人类和聊天机器人的互动性，尤其是当代理人需要回答预定义角色中排除的查询时，这是如此 - 被称为预先定义的角色问题（以简单性为OOP）。为了减轻问题，在本文中，我们提出了一个新颖的检索到预测范式，该范式由两个子组件组成，即（1）角色检索模型（PRM），它根据自然语言推论从全球收藏中检索角色（ NLI）模型，推断的角色与预定义的角色一致； （2）后验变压器（PS-Transformer）采用角色后部分布，进一步考虑了地面响应中使用的实际角色，从而最大程度地减轻了训练和推断之间的差距。此外，我们提出了一个名为IT-Convai2的数据集，该数据集首先突出了个性化对话中的OOP问题。对IT-Convai2和Convai2的广泛实验表明，我们提出的模型在自动指标和人类评估方面都有显着改善。

在3D视觉中，视觉重新定位已被广泛讨论：鉴于预构建的3D视觉图，估计查询图像的6 DOF（自由度）姿势。大规模室内环境中的重新定位可实现有吸引力的应用程序，例如增强现实和机器人导航。但是，当相机移动时，在这种环境中，外观变化很快，这对于重新定位系统来说是具有挑战性的。为了解决这个问题，我们建议一种基于虚拟视图综合方法Rendernet，以丰富有关此特定情况的数据库和完善姿势。我们选择直接渲染虚拟观点的必要全局和本地特征，而不是渲染需要高质量3D模型的真实图像，并分别将它们应用于后续图像检索和功能匹配操作中。所提出的方法在很大程度上可以改善大规模室内环境中的性能，例如，在INLOC数据集中获得7.1 \％和12.2 \％的改善。

随着Covid-19的爆发，近年来已经出现了大量相关研究。我们提出了一个基于肺CT扫描图像的自动COVID-19诊断框架，即PVT-COV19D。为了适应图像输入的不同维度，我们首先使用变压器模型对图像进行了分类，然后根据正常分布对数据集中进行采样，并将采样结果馈送到修改的PVTV2模型中以进行训练。COV19-CT-DB数据集上的大量实验证明了该方法的有效性。

This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.

To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

建模语义信息对于场景文本识别有用。在这项工作中，我们建议与视觉语义变压器（VST）共同模拟语义和视觉信息。 VST首先从具有变压器模块和主视觉语义对齐模块中的视觉特征映射明确地提取主语义信息。然后将语义信息与视觉特征映射（被视为序列）连接以形成伪多域序列，该伪多域序列组合视觉和语义信息，随后将其馈入基于变压器的交互模块，以便能够在视觉和视觉之间学习相互作用语义特征。以这种方式，可以通过语义信息和反之亦然可以增强视觉特征。可视特征的增强版本通过辅助视觉 - 语义对准模块进一步解码，其与主要一个共享权重。最后，通过获得最终文本预测的第三变压器模块共同处理解码的视觉特征和增强的语义特征。在包括常规/不规则文本识别数据集的七个公共基准测试中的实验验证了我们所提出的模型，在七个基准中的四个基准中达到最先进的效果。

在广泛的实用应用中，需要进行远程感知的城市场景图像的语义细分，例如土地覆盖地图，城市变化检测，环境保护和经济评估。在深度学习技术的快速发展，卷积神经网络（CNN）的迅速发展。 ）多年来一直在语义细分中占主导地位。 CNN采用层次特征表示，证明了局部信息提取的强大功能。但是，卷积层的本地属性限制了网络捕获全局上下文。最近，作为计算机视觉领域的热门话题，Transformer在全球信息建模中展示了其巨大的潜力，从而增强了许多与视觉相关的任务，例如图像分类，对象检测，尤其是语义细分。在本文中，我们提出了一个基于变压器的解码器，并为实时城市场景细分构建了一个类似Unet的变压器（UneTformer）。为了有效的分割，不显示器将轻量级RESNET18选择作为编码器，并开发出有效的全球关注机制，以模拟解码器中的全局和局部信息。广泛的实验表明，我们的方法不仅运行速度更快，而且与最先进的轻量级模型相比，其准确性更高。具体而言，拟议的未显示器分别在无人机和洛夫加数据集上分别达到了67.8％和52.4％的MIOU，而在单个NVIDIA GTX 3090 GPU上输入了512x512输入的推理速度最多可以达到322.4 fps。在进一步的探索中，拟议的基于变压器的解码器与SWIN变压器编码器结合使用，还可以在Vaihingen数据集上实现最新的结果（91.3％F1和84.1％MIOU）。源代码将在https://github.com/wanglibo1995/geoseg上免费获得。