Data Augmentation (DA) is frequently used to automatically provide additional training data without extra human annotation. However, data augmentation may introduce noisy data that impairs training. To guarantee the quality of augmented data, existing methods either assume no noise exists in the augmented data and adopt consistency training or use simple heuristics such as training loss and diversity constraints to filter out ``noisy'' data. However, those filtered examples may still contain useful information, and dropping them completely causes loss of supervision signals. In this paper, based on the assumption that the original dataset is cleaner than the augmented data, we propose an on-the-fly denoising technique for data augmentation that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original data. A simple self-regularization module is applied to force the model prediction to be consistent across two distinct dropouts to further prevent overfitting on noisy labels. Our method can be applied to augmentation techniques in general and can consistently improve the performance on both text classification and question-answering tasks.

The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to capture the hierarchical patterns due to its tree-like metrics, which addressed the limitations of the Euclidean embedding models. Recent explorations of the complex hyperbolic geometry further improved the hyperbolic embeddings for capturing a variety of hierarchical structures. However, the performance of the hyperbolic KG embedding models for non-transitive relations is still unpromising, while the complex hyperbolic embeddings do not deal with multi-relations. This paper aims to utilize the representation capacity of the complex hyperbolic geometry in multi-relational KG embeddings. To apply the geometric transformations which account for different relations and the attention mechanism in the complex hyperbolic space, we propose to use the fast Fourier transform (FFT) as the conversion between the real and complex hyperbolic space. Constructing the attention-based transformations in the complex space is very challenging, while the proposed Fourier transform-based complex hyperbolic approaches provide a simple and effective solution. Experimental results show that our methods outperform the baselines, including the Euclidean and the real hyperbolic embedding models.

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG.

微调可能容易受到对抗攻击的影响。现有有关对微调模型（BAFT）的黑盒攻击的作品受到强有力的假设的限制。为了填补空白，我们提出了两个新型的BAFT设置，即跨域和跨域交叉结构BAFT，这仅假设（1）攻击的目标模型是微调模型，以及（2）源域数据是已知和可访问的。为了成功攻击两种设置下的微调模型，我们建议先训练针对源模型的对抗发电机，该模型采用编码器架构体系结构并将干净的输入映射到对抗性示例。然后，我们在对抗发电机的编码器产生的低维潜在空间中搜索。搜索是根据从源模型获得的替代梯度的指导进行的。对不同域和不同网络体系结构的实验结果表明，提出的攻击方法可以有效，有效地攻击微调模型。

随着图表和图表学习的开发，已经提出了许多优越的方法来处理图形结构学习的可扩展性和过度厚度问题。但是，大多数策略都是基于实践经验而不是理论分析而设计的。在本文中，我们使用连接到所有现有顶点的特定虚拟节点，而不会影响原始顶点和边缘属性。我们进一步证明，这种虚拟节点可以帮助构建有效的单态边缘到vertex变换，并呈现呈呈倒数，以恢复原始图。这也表明，添加虚拟节点可以保留本地和全局结构，以更好地图表表示。我们扩展了具有虚拟节点的图形内核和图形神经网络，并在图形分类和子图同构匹配任务上进行实验。经验结果表明，以虚拟节点为输入的图表显着增强了图形结构学习，并且使用其边缘到vertex图也可以实现相似的结果。我们还讨论了神经网络中假人的表达能力的增长。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

图表神经网络（GNN）和消息通过神经网络（MPNNS）被证明是在许多应用中的子图结构中表达的。异构图中的一些应用需要明确的边缘建模，例如子图同样计数和匹配。但是，现有的消息传递机制在理论上并不良好设计。在本文中，我们从特定的边缘到顶点变换开始，利用边缘到顶点双图中的同义性属性。我们证明，搜索原始图中的同构相当于在其双图上搜索。基于该观察，我们提出了通过神经网络（DMPNNS）的双信息以异步方式增强子图同样计数和匹配以及无监督的节点分类。广泛的实验通过在合成和真实异构图中结合节点和边缘表示学习来证明DMPNN的稳健性能。代码可在https://github.com/hkust-knowcomp/dualmessagepass上获得。

复杂查询应答（CQA）是知识图中的一个重要推理任务。目前已经证明能够从原子操作员概括到更复杂的公式中的当前CQA学习模型，这可以被视为组合概括性。在本文中，我们呈现EFO-1-QA，通过包括301种不同的查询类型来基准CQA模型的组合概括性的EFO-1-QA来基准，这是比现有数据集大的20倍。此外，我们的工作首次提供基准来评估和分析不同运营商和正常形式的影响，通过使用（a）7个选择的操作系统和（b）9形式的复杂查询。具体地，我们提供了两个常用的运营商的组合概括性的详细研究，即投影和交叉点，并证明了鉴于运营商的规范选择的疑问形式的影响。我们的代码和数据可以为基准CQA模型提供有效的管道。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.