Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages. In this work, we investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages. We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms. Such difference learnt via self-supervision plays a crucial role in the zero-shot transfer performance and can be predicted by variation in morphosyntactic properties between languages. These results suggest that mBERT properly encodes languages in a way consistent with linguistic diversity and provide insights into the mechanism of cross-lingual transfer.
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我们展示了一个新的开源和可扩展知识提取工具包，称为Deepke（基于深度学习的知识提取），支持标准完全监督，低资源少拍摄和文档级方案。 Deepke实现了各种信息提取任务，包括命名实体识别，关系提取和属性提取。使用统一的框架，DeePke允许开发人员和研究人员根据其要求，自定义数据集和模型以从非结构化文本中提取信息。具体而言，DeePke不仅为不同的任务和场景提供了各种功能模块和模型实现，而且还通过一致的框架组织所有组件以维持足够的模块化和可扩展性。此外，我们在\ URL {http://deepke.zjukg.cn/}中介绍一个在线平台，用于实时提取各种任务。 Deepke已经配备了Google Colab教程和初学者的综合文件。我们用演示视频发布\ url {https://github.com/zjunlp/deepke}源代码。
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大多数NER方法都依赖于广泛的标记数据进行模型培训，这些数据在低资源场景中挣扎，培训数据有限。与资源丰富的源域相比，现有的主要方法通常会遇到目标域具有不同标签集的挑战，该标签集可以作为类传输和域转移得出的结论。在本文中，我们通过可拔出的提示（Lightner）提出了一个轻巧的调整范式，用于低资源。具体而言，我们构建了实体类别的统一可学习的语言器，以生成实体跨度序列和实体类别，而无需任何标签特定的分类器，从而解决了类转移问题。我们通过将可学习的参数纳入自我发言层作为指导，进一步提出了一个可插入的指导模块，该参数可以重新调节注意力并调整预训练的权重。请注意，我们仅通过修复了预训练的语言模型的整个参数来调整那些插入的模块，从而使我们的方法轻巧且灵活地适合低资源场景，并且可以更好地跨域传输知识。实验结果表明，Lightner可以在标准监督环境中获得可比的性能，并且在低资源设置中优于强大基线。代码在https://github.com/zjunlp/deepke/tree/main/main/example/ner/few-shot中。
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2023-01-02
This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.
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Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions.
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Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
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Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance.
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基于骨架的人类行动识别是由于其复杂的动态而是一项长期挑战。动态的一些细颗粒细节在分类中起着至关重要的作用。现有的工作主要集中在设计带有更复杂的相邻矩阵的增量神经网络上，以捕获关节关系的细节。但是，他们仍然很难区分具有广泛相似运动模式但属于不同类别的动作。有趣的是，我们发现运动模式上的细微差异可以显着放大，并且可以轻松地通过指定的视图方向来区分观众，在这些方向上，该属性以前从未得到充分探索。与以前的工作截然不同，我们通过提出一种概念上简单而有效的多视图策略来提高性能，该策略从一系列动态视图功能中识别动作。具体而言，我们设计了一个新颖的骨骼锚定建议（SAP）模块，该模块包含一个多头结构来学习一组视图。为了学习不同观点的特征学习，我们引入了一个新的角度表示，以在不同视图下的动作转换并将转换归因于基线模型。我们的模块可以与现有的动作分类模型无缝合作。与基线模型合并，我们的SAP模块在许多具有挑战性的基准上展示了明显的性能增长。此外，全面的实验表明，我们的模型始终击败了最新的实验，并且在处理损坏的数据时保持有效和健壮。相关代码将在https://github.com/ideal-idea/sap上提供。
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视觉问题回答（VQA）通常需要对视觉概念和语言语义的理解，这取决于外部知识。大多数现有方法利用了预训练的语言模型或/和非结构化文本，但是这些资源中的知识通常不完整且嘈杂。有些方法更喜欢使用经常具有强化结构知识的知识图（kgs），但是研究仍然相当初步。在本文中，我们提出了Lako，这是一种知识驱动的VQA方法，通过后期的文本注射。为了有效地纳入外部kg，我们将三元三元转移到文本中，并提出一种晚期注射机制。最后，我们将VQA作为文本生成任务，并具有有效的编码器范式。在使用OKVQA数据集的评估中，我们的方法可实现最新的结果。
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迅速的学习方法通过诱导更好的几次表现，在他们仍然遵循基于参数的学习范式的同时，引起了自然语言处理的波动。学习中的遗忘和死记硬背的记忆问题可能会遇到不稳定的概括问题。具体而言，香草及时的学习可能难以利用死记硬背的非典型实例，在完全监督的培训或过度贴身模式的情况下使用低射击数据。为了减轻此类局限性，我们以将知识从记忆中解耦的动机发展为有助于模型在概括和记忆之间取得平衡。与香草及时学习相反，重新启动构造了培训实例中的开放式知识店，并在输入，培训和推理过程中实现检索机制，从而使该模型能够从培训语料库中检索相关环境作为能力为提示增强。广泛的实验表明，Retroppt可以在几次射击和零拍设置中获得更好的性能。此外，我们进一步说明，我们提出的撤退可以通过新数据集获得更好的概括能力。对记忆的详细分析确实显示逆转可以减少语言模型对记忆的依赖；因此，改善下游任务的概括。
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