Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.

我们提出了一个零射门学习关系分类（ZSLRC）框架，通过其识别训练数据中不存在的新颖关系的能力来提高最先进的框架。零射击学习方法模仿人类学习和识别新概念的方式，没有先前的知识。为此，ZSLRC使用修改的高级原型网络来利用加权侧（辅助）信息。 ZSLRC的侧面信息是由关键字，名称实体的高度和标签及其同义词构建的。 ZSLRC还包括一个自动高义的提取框架，可直接从Web获取各种名称实体的高型。 ZSLRC提高了最先进的少量学习关系分类方法，依赖于标记的培训数据，因此即使在现实世界方案中也适用于某些关系对相应标记的培训示例。我们在两种公共数据集（NYT和NEREREL）上使用广泛的实验显示结果，并显示ZSLRC显着优于最先进的方法对监督学习，少量学习和零射击学习任务。我们的实验结果还展示了我们所提出的模型的有效性和稳健性。

Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). Most IE systems require comprehensive understandings of sentence structure, implied semantics, and domain knowledge to perform well; thus, IE tasks always need adequate external resources and annotations. However, it takes time and effort to obtain more human annotations. Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation. In practice, existing systems either utilize self-training schemes to generate pseudo labels that will cause the gradual drift problem, or leverage consistency regularization methods which inevitably possess confirmation bias. To alleviate confirmation bias due to the lack of feedback loops in existing LRIE learning paradigms, we develop a Gradient Imitation Reinforcement Learning (GIRL) method to encourage pseudo-labeled data to imitate the gradient descent direction on labeled data, which can force pseudo-labeled data to achieve better optimization capabilities similar to labeled data. Based on how well the pseudo-labeled data imitates the instructive gradient descent direction obtained from labeled data, we design a reward to quantify the imitation process and bootstrap the optimization capability of pseudo-labeled data through trial and error. In addition to learning paradigms, GIRL is not limited to specific sub-tasks, and we leverage GIRL to solve all IE sub-tasks (named entity recognition, relation extraction, and event extraction) in low-resource settings (semi-supervised IE and few-shot IE).

As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.

Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct class representations in different ways, varying from mean output embedding to covariance and distributions. However, using embeddings in space lacks expressivity and cannot capture class information robustly, while statistical complex modeling poses difficulty to metric designs. In this work, we use tensor fields (``areas'') to model classes from the geometrical perspective for few-shot learning. We present a simple and effective method, dubbed hypersphere prototypes (HyperProto), where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere's center and the radius. Extending from points to areas, hyperspheres are much more expressive than embeddings. Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere. Following this idea, we also develop two variants of prototypes under other measurements. Extensive experiments and analysis on few-shot learning tasks across NLP and CV and comparison with 20+ competitive baselines demonstrate the effectiveness of our approach.

与伯特（Bert）等语言模型相比，已证明知识增强语言表示的预培训模型在知识基础构建任务（即〜关系提取）中更有效。这些知识增强的语言模型将知识纳入预训练中，以生成实体或关系的表示。但是，现有方法通常用单独的嵌入表示每个实体。结果，这些方法难以代表播出的实体和大量参数，在其基础代币模型之上（即〜变压器），必须使用，并且可以处理的实体数量为由于内存限制，实践限制。此外，现有模型仍然难以同时代表实体和关系。为了解决这些问题，我们提出了一个新的预培训模型，该模型分别从图书中学习实体和关系的表示形式，并分别在文本中跨越跨度。通过使用SPAN模块有效地编码跨度，我们的模型可以代表实体及其关系，但所需的参数比现有模型更少。我们通过从Wikipedia中提取的知识图对我们的模型进行了预训练，并在广泛的监督和无监督的信息提取任务上进行了测试。结果表明，我们的模型比基线学习对实体和关系的表现更好，而在监督的设置中，微调我们的模型始终优于罗伯塔，并在信息提取任务上取得了竞争成果。

关系提取（RE）是自然语言处理的基本任务。RE试图通过识别文本中的实体对之间的关系信息来将原始的，非结构化的文本转变为结构化知识。RE有许多用途，例如知识图完成，文本摘要，提问和搜索查询。RE方法的历史可以分为四个阶段：基于模式的RE，基于统计的RE，基于神经的RE和大型语言模型的RE。这项调查始于对RE的早期阶段的一些示例性作品的概述，突出了局限性和缺点，以使进度相关。接下来，我们回顾流行的基准测试，并严格检查用于评估RE性能的指标。然后，我们讨论遥远的监督，这是塑造现代RE方法发展的范式。最后，我们回顾了重点是降级和培训方法的最新工作。

主动学习（al）试图通过标记最少的样本来最大限度地提高模型的性能增益。深度学习（DL）是贪婪的数据，需要大量的数据电源来优化大量参数，因此模型了解如何提取高质量功能。近年来，由于互联网技术的快速发展，我们处于信息种类的时代，我们有大量的数据。通过这种方式，DL引起了研究人员的强烈兴趣，并已迅速发展。与DL相比，研究人员对Al的兴趣相对较低。这主要是因为在DL的崛起之前，传统的机器学习需要相对较少的标记样品。因此，早期的Al很难反映其应得的价值。虽然DL在各个领域取得了突破，但大多数这一成功都是由于大量现有注释数据集的宣传。然而，收购大量高质量的注释数据集消耗了很多人力，这在某些领域不允许在需要高专业知识，特别是在语音识别，信息提取，医学图像等领域中， al逐渐受到适当的关注。自然理念是AL是否可用于降低样本注释的成本，同时保留DL的强大学习能力。因此，已经出现了深度主动学习（DAL）。虽然相关的研究非常丰富，但它缺乏对DAL的综合调查。本文要填补这一差距，我们为现有工作提供了正式的分类方法，以及全面和系统的概述。此外，我们还通过申请的角度分析并总结了DAL的发展。最后，我们讨论了DAL中的混乱和问题，为DAL提供了一些可能的发展方向。

The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world. This thesis works on Drug-Drug Interactions (DDIs) from the literature as a case study and realizes relation extraction utilizing heterogeneous domain information. First, a deep neural relation extraction model is prepared and its attention mechanism is analyzed. Next, a method to combine the drug molecular structure information and drug description information to the input sentence information is proposed, and the effectiveness of utilizing drug molecular structures and drug descriptions for the relation extraction task is shown. Then, in order to further exploit the heterogeneous information, drug-related items, such as protein entries, medical terms and pathways are collected from multiple existing databases and a new data set in the form of a knowledge graph (KG) is constructed. A link prediction task on the constructed data set is conducted to obtain embedding representations of drugs that contain the heterogeneous domain information. Finally, a method that integrates the input sentence information and the heterogeneous KG information is proposed. The proposed model is trained and evaluated on a widely used data set, and as a result, it is shown that utilizing heterogeneous domain information significantly improves the performance of relation extraction from the literature.

命名实体识别（ner）旨在标识在非结构化文本中的命名实体的提到，并将它们分类为预定义的命名实体类。尽管基于深度学习的预先训练的语言模型实现了良好的预测性能，但许多域特定的NERTASK仍然需要足够量的标记数据。主动学习（AL）是标签采集问题的一般框架，已用于NER任务，以最大限度地降低注释成本而不会牺牲模型性能。然而，令牌的严重不平衡的课程分布引入了设计有效的NER Querying方法的挑战。我们提出了al句子查询评估函数，这些函数更加关注可能的积极令牌，并评估基于句子和基于令牌的成本评估策略的这些提出的功能。我们还提出了更好的数据驱动的归一化方法来惩罚太长或太短的句子。我们在来自不同域的三个数据集上的实验表明，所提出的方法减少了带有常规方法的更好或可比预测性能的增注令牌的数量。

我们研究了用于半监控学习（SSL）的无监督数据选择，其中可以提供大规模的未标记数据集，并且为标签采集预算小额数据子集。现有的SSL方法专注于学习一个有效地集成了来自给定小标记数据和大型未标记数据的信息的模型，而我们专注于选择正确的数据以用于SSL的注释，而无需任何标签或任务信息。直观地，要标记的实例应统称为下游任务的最大多样性和覆盖范围，并且单独具有用于SSL的最大信息传播实用程序。我们以三步数据为中心的SSL方法形式化这些概念，使稳定性和精度的纤维液改善8％的CiFar-10（标记为0.08％）和14％的Imagenet -1k（标记为0.2％）。它也是一种具有各种SSL方法的通用框架，提供一致的性能增益。我们的工作表明，在仔细选择注释数据上花费的小计算带来了大注释效率和模型性能增益，而无需改变学习管道。我们完全无监督的数据选择可以轻松扩展到其他弱监督的学习设置。

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

监督学习已被广​​泛用于攻击分类，需要高质量的数据和标签。但是，数据通常是不平衡的，很难获得足够的注释。此外，有监督的模型应遵守现实世界的部署问题，例如防御看不见的人造攻击。为了应对挑战，我们提出了一个半监督的细粒攻击分类框架，该框架由编码器和两个分支机构结构组成，并且该框架可以推广到不同的监督模型。具有残留连接的多层感知器用作提取特征并降低复杂性的编码器。提出了复发原型模块（RPM）以半监督的方式有效地训练编码器。为了减轻数据不平衡问题，我们将重量任务一致性（WTC）引入RPM的迭代过程中，通过将较大的权重分配给损失函数中较少样本的类别。此外，为了应对现实世界部署中的新攻击，我们提出了一种主动调整重新采样（AAR）方法，该方法可以更好地发现看不见的样本数据的分布并调整编码器的参数。实验结果表明，我们的模型优于最先进的半监督攻击检测方法，分类精度提高了3％，训练时间降低了90％。

生物医学机器阅读理解（生物医学MRC）旨在理解复杂的生物医学叙事，并协助医疗保健专业人员从中检索信息。现代神经网络的MRC系统的高性能取决于高质量的大规模，人为宣传的培训数据集。在生物医学领域中，创建此类数据集的一个至关重要的挑战是域知识的要求，引起了标记数据的稀缺性以及从标记的通用（源）域转移学习到生物医学（目标）域的需求。然而，由于主题方差，通用和生物医学领域之间的边际分布存在差异。因此，从在通用域上训练的模型到生物医学领域的模型直接转移学会的表示可能会损害模型的性能。我们为生物医学机器阅读理解任务（BioAdapt-MRC）提供了基于对抗性学习的域适应框架，这是一种基于神经网络的方法，可解决一般和生物医学域数据之间边际分布中的差异。 Bioadapt-MRC松弛了生成伪标签的需求，以训练表现出色的生物医学MRC模型。我们通过将生物ADAPT-MRC与三种广泛使用的基准生物医学MRC数据集进行比较，从而广泛评估了生物ADAPT-MRC的性能-Bioasq-7B，BioASQ-8B和BioASQ-9B。我们的结果表明，如果不使用来自生物医学领域的任何合成或人类通知的数据，Bioadapt-MRC可以在这些数据集中实现最先进的性能。可用性：bioadapt-MRC可作为开放源项目免费获得，\ url {https://github.com/mmahbub/bioadapt-mrc}。

作为人类认知的重要组成部分，造成效果关系频繁出现在文本中，从文本策划原因关系有助于建立预测任务的因果网络。现有的因果关系提取技术包括基于知识的，统计机器学习（ML）和基于深度学习的方法。每种方法都具有其优点和缺点。例如，基于知识的方法是可以理解的，但需要广泛的手动域知识并具有较差的跨域适用性。由于自然语言处理（NLP）工具包，统计机器学习方法更加自动化。但是，功能工程是劳动密集型的，工具包可能导致错误传播。在过去的几年里，由于其强大的代表学习能力和计算资源的快速增加，深入学习技术吸引了NLP研究人员的大量关注。它们的局限包括高计算成本和缺乏足够的注释培训数据。在本文中，我们对因果关系提取进行了综合调查。我们最初介绍了因果关系提取中存在的主要形式：显式的内部管制因果关系，隐含因果关系和间情态因果关系。接下来，我们列出了代理关系提取的基准数据集和建模评估方法。然后，我们介绍了三种技术的结构化概述了与他们的代表系统。最后，我们突出了潜在的方向存在现有的开放挑战。

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The source code and experiment details of this paper can be obtained from https:// github.com/thunlp/ERNIE.

建模法检索和检索作为预测问题最近被出现为法律智能的主要方法。专注于法律文章检索任务，我们展示了一个名为Lamberta的深度学习框架，该框架被设计用于民法代码，并在意大利民法典上专门培训。为了我们的知识，这是第一项研究提出了基于伯特（来自变压器的双向编码器表示）学习框架的意大利法律制度对意大利法律制度的高级法律文章预测的研究，最近引起了深度学习方法的增加，呈现出色的有效性在几种自然语言处理和学习任务中。我们通过微调意大利文章或其部分的意大利预先训练的意大利预先训练的伯爵来定义Lamberta模型，因为法律文章作为分类任务检索。我们Lamberta框架的一个关键方面是我们构思它以解决极端的分类方案，其特征在于课程数量大，少量学习问题，以及意大利法律预测任务的缺乏测试查询基准。为了解决这些问题，我们为法律文章的无监督标签定义了不同的方法，原则上可以应用于任何法律制度。我们提供了深入了解我们Lamberta模型的解释性和可解释性，并且我们对单一标签以及多标签评估任务进行了广泛的查询模板实验分析。经验证据表明了Lamberta的有效性，以及对广泛使用的深度学习文本分类器和一些构思的几次学习者来说，其优越性是对属性感知预测任务的优势。

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER) and disambiguation (NED), RE forms the basis for many advanced IE tasks such as knowledge-base (KB) population and verification. In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences' principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. Our main conjecture is that the decomposition of long and possibly convoluted sentences into multiple smaller clauses via OpenIE even helps to fine-tune context-sensitive language models such as BERT (and its plethora of variants) for RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models compared to existing RE approaches. Our best results reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively, proving the effectiveness of our approach on competitive benchmarks.

本文解决了几秒钟学习问题，旨在从几个例子中学习新的视觉概念。在几次拍摄分类中的常见问题设置假设在获取数据标签中的随机采样策略，其在实际应用中效率低下。在这项工作中，我们介绍了一个新的预算感知几秒钟学习问题，不仅旨在学习新的对象类别，还需要选择信息实例来注释以实现数据效率。我们为我们的预算感知几秒钟学习任务开发了一个元学习策略，该任务共同了解基于图形卷积网络（GCN）和基于示例的少量拍摄分类器的新型数据选择策略。我们的选择策略通过图形消息传递计算每个未标记数据的上下文敏感表示，然后用于预测顺序选择的信息性分数。我们在迷你想象网，分层 - 想象项目和omniglot数据集上进行广泛的实验验证我们的方法。结果表明，我们的几次学习策略优于一个相当大的边缘，这表明了我们的方法的功效。