跨语言嵌入（CLWE）已被证明在许多跨语性任务中有用。但是，大多数现有的学习Clwe的方法，包括具有上下文嵌入的方法是无知的。在这项工作中，我们提出了一个新颖的框架，以通过仅利用双语词典的跨语性信号来使上下文嵌入在感觉层面上。我们通过首先提出一种新颖的感知感知的跨熵损失来明确地提出一种新颖的感知跨熵损失来实现我们的框架。通过感知感知的跨熵损失预算的单语Elmo和BERT模型显示出对单词感官歧义任务的显着改善。然后，我们提出了一个感官对齐目标，除了跨语义模型预训练的感知感知跨熵损失以及几种语言对的跨语义模型（英语对德语/西班牙语/日本/中文）。与最佳的基线结果相比，我们的跨语言模型分别在零摄影，情感分类和XNLI任务上达到0.52％，2.09％和1.29％的平均绩效提高。

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.

Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED include how well existing ED models perform on different languages, how challenging ED is in other languages, and how well ED knowledge and annotation can be transferred across languages. To answer those questions, it is crucial to obtain multilingual ED datasets that provide consistent event annotation for multiple languages. There exist some multilingual ED datasets; however, they tend to cover a handful of languages and mainly focus on popular ones. Many languages are not covered in existing multilingual ED datasets. In addition, the current datasets are often small and not accessible to the public. To overcome those shortcomings, we introduce a new large-scale multilingual dataset for ED (called MINION) that consistently annotates events for 8 different languages; 5 of them have not been supported by existing multilingual datasets. We also perform extensive experiments and analysis to demonstrate the challenges and transferability of ED across languages in MINION that in all call for more research effort in this area.

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE.

3D inference from monocular vision using neural networks is an important research area of computer vision. Applications of the research area are various with many proposed solutions and have shown remarkable performance. Although many efforts have been invested, there are still unanswered questions, some of which are fundamental. In this paper, I discuss a problem that I hope will come to be known as a generalization of the Blind Perspective-n-Point (Blind PnP) problem for object-driven 3D inference based on 2D representations. The vital difference between the fundamental problem and the Blind PnP problem is that 3D inference parameters in the fundamental problem are attached directly to 3D points and the camera concept will be represented through the sharing of the parameters of these points. By providing an explainable and robust gradient-decent solution based on 2D representations for an important special case of the problem, the paper opens up a new approach for using available information-based learning methods to solve problems related to 3D object pose estimation from 2D images.

Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood. In this paper, we develop a novel framework to study the stability and generalization of these optimization methods. Based on this framework, we show provable guarantees about such properties that depend heavily on a single parameter $\beta_2$. Our empirical experiments support our claims and provide practical insights into the stability and generalization properties of adaptive optimization methods.

Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations. In addition, we introduce Adaptive Weighting scheme for our contrastive learning approach in order to increase flexibility in optimization. Lastly, we propose Multimodal Interaction module to address the unalignment nature of multimodal data, thereby assisting the model in producing more reasonable multimodal representations. Experimental results show that our method outperforms prior baselines and achieves state-of-the-art results on two publicly available benchmark datasets for MRHP problem.

Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particular, we propose a method for partially observable reinforcement learning that uses a fully observable policy (which we call a state expert) during offline training to improve online performance. Based on Soft Actor-Critic (SAC), our agent balances performing actions similar to the state expert and getting high returns under partial observability. Our approach can leverage the fully-observable policy for exploration and parts of the domain that are fully observable while still being able to learn under partial observability. On six robotics domains, our method outperforms pure imitation, pure reinforcement learning, the sequential or parallel combination of both types, and a recent state-of-the-art method in the same setting. A successful policy transfer to a physical robot in a manipulation task from pixels shows our approach's practicality in learning interesting policies under partial observability.

流视频是创作者与观众分享创意作品的方法之一。在这些视频中，流媒体分享了如何通过在一个或几个用于创意项目的程序中使用各种工具来实现最终目标。为此，可以讨论实现最终目标所需的步骤。因此，这些视频可以提供大量的教育内容，这些内容可用于学习如何使用流媒体使用的工具。但是，缺点之一是，流媒体可能无法为每个步骤提供足够的详细信息。因此，对于学习者来说，可能很难赶上所有步骤。为了减轻此问题，一种解决方案是将流视频与流视频中使用的工具可用的相关教程联系起来。更具体地说，系统可以分析实时流媒体视频的内容，并推荐最相关的教程。由于现有的文档推荐模型无法处理这种情况，因此在这项工作中，我们为实时流程视频的教程建议提供了一个新颖的数据集和模型。我们对拟议的数据集和模型进行了广泛的分析，揭示了该任务的挑战性质。

键形提取是NLP中文档理解的重要任务之一。虽然大多数先前的作品都致力于正式设置，例如书籍，新闻或网络博客，但探索视频成绩单等非正式文本的探索较少。为了解决这一局限性，在这项工作中，我们提出了一种新颖的语料库和方法，用于从Behance平台上流的视频的成绩单中提取钥匙短语。更具体地说，在这项工作中，提出了一种新型的数据增强，以通过从其他域中提取键形提取任务的背景知识来丰富模型。提出的数据集数据集上的广泛实验显示了引入方法的有效性。