实体集扩展（ESE）是一项有价值的任务，旨在找到给定种子实体描述的目标语义类别的实体。由于其发现知识的能力，各种NLP和下游应用程序都受益于ESE。尽管现有的引导方法取得了巨大进展，但其中大多数仍然依赖手动预定义的上下文模式。预定义的上下文模式的不可忽略的缺点是，它们不能灵活地推广到各种语义类别，我们将这种现象称为“语义敏感性”。为了解决这个问题，我们设计了一个上下文模式生成模块，该模块利用自回归语言模型（例如GPT-2）自动为实体生成高质量的上下文模式。此外，我们提出了GAPA，这是一种新型ESE框架，利用上述生成的模式扩展目标实体。对三个广泛使用的数据集进行了广泛的实验和详细分析，证明了我们方法的有效性。我们实验的所有代码都将用于可重复性。

Pro-Drop通常在许多语言中都可以看出，但其话语动机并没有得到很好的特征。受到中文主题链理论的启发，本研究表明了角色 - 动词的使用连续性如何将删除的代词与公开引用对故事人物的范围区分开来。我们对掉落而不是掉落作为字符 - 动词连续性的函数进行建模。结果表明，省略的受试者具有比非主题的受试者更高的字符历史流动连续性显着性。这与这样的想法是一致的，即话语与特定主题（例如故事特征）的一致性确实有助于在其可选的语言和上下文中省略代词。

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA Pro, Overnight, GrailQA, and MetaQA-Cypher under standard i.i.d., out-of-distribution, and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement.

基于对专家的声音模型，具有动态路由机制已经证明了语音识别的有希望的结果。路由器架构的设计原理对于大型型号容量和高计算效率很重要。我们以前的工作Speepmoe仅使用本地图形嵌入嵌入来帮助路由器进行路由决策。为了进一步提高语音识别性能，反对不同的域和重音，我们提出了一种新的路由器架构，该架构将额外的全局域和重点嵌入路由器输入以促进适应性。实验结果表明，所提出的Speepmoe2可以实现比较参数的较低字符的误差率（CER），而不是多域和多重点任务上的Spearmmoe。主要是，拟议的方法分别提供多元域任务的相对12.8％的相对元改善，分别为多重点任务的相对经济增长1.9％-17.7％。此外，增加专家人数也取得了一致的性能改进，并保持计算成本不变。

在知识库（复杂KBQA）上回答的复杂问题是具有挑战性的，因为它需要各种组成推理功能，例如多跳推断，属性比较，集合操作。现有的基准有一些缺点，这些缺点限制了复杂的KBQA的发展：1）它们仅提供质量检查对而没有明确的推理过程； 2）问题的多样性或规模很差。为此，我们介绍了KQA Pro，这是一个用于复杂KBQA的数据集，包括〜120k多样化的自然语言问题。我们引入了一种构图和可解释的编程语言KOPL，以表示复杂问题的推理过程。对于每个问题，我们都提供相应的KOPL程序和SPARQL查询，因此KQA Pro可用于KBQA和语义解析任务。实验结果表明，SOTA KBQA方法无法像当前数据集上的KQA Pro上实现有希望的结果，这表明KQA Pro具有挑战性，复杂的KBQA需要进一步的研究工作。我们还将KQA Pro视为用于测试多种推理技能的诊断数据集，对现有模型进行彻底评估，并讨论复杂KBQA的进一步说明。我们的代码和数据集可以从https://github.com/shijx12/kqapro_baselines获得。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.