Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.
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Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts. To address this problem, there are two related tasks: OKB canonicalization (i.e., convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and RPs with their corresponding entities and relations in a curated Knowledge Base (e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit significantly from the other. However, they have been studied in isolation so far. In this paper, we explore the task of joint OKB canonicalization and linking for the first time, and propose a novel framework JOCL based on factor graph model to make them reinforce each other. JOCL is flexible enough to combine different signals from both tasks, and able to extend to fit any new signals. A thorough experimental study over two large scale OIE triple data sets shows that our framework outperforms all the baseline methods for the task of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).
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Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document. One major challenge of DocRE is to dig decisive details regarding a specific entity pair from long text. However, in many cases, only a fraction of text carries required information, even in the manually labeled supporting evidence. To better capture and exploit instructive information, we propose a novel expLicit syntAx Refinement and Subsentence mOdeliNg based framework (LARSON). By introducing extra syntactic information, LARSON can model subsentences of arbitrary granularity and efficiently screen instructive ones. Moreover, we incorporate refined syntax into text representations which further improves the performance of LARSON. Experimental results on three benchmark datasets (DocRED, CDR, and GDA) demonstrate that LARSON significantly outperforms existing methods.
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事件检测任务可以帮助人们快速从复杂文本中确定域。它还可以为自然语言处理的下游任务提供强大的支持。存在仅基于大量数据实现固定型学习。当扩展到新课程时,通常需要保留原始数据并重新训练模型。事件检测任务可以终身学习新类,但是大多数现有方法都需要保留大量原始数据或面临灾难性的问题忘记。除此之外,由于缺乏实用性数据,很难获得足够的数据进行模型培训。要解决上述问题,我们在事件检测的领域定义了一项新任务,这是很少的增量事件检测。此任务要求在学习新事件类型的情况下,该模型应保留以前的类型,并且输入有限。我们根据几个event重新创建和发布基准数据集,以少数数量的事件检测任务。我们发布的数据集比该新任务中的其他数据集更合适。此外,我们提出了两种基准方法,即IFSED-K和IFSED-KP,可以以不同的方式解决任务。实验结果表明,我们的方法具有更高的F1分数,并且比基线更稳定。
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实体链接(EL)是将实体提及在文本中及其相应实体中出现在知识库中的过程。通常基于Wikipedia估算实体的EL特征(例如,先前的概率,相关性评分和实体嵌入)。但是,对于刚刚在新闻中发现的新兴实体(EES)而言,它们可能仍未包含在Wikipedia中。结果,它无法获得Wikipedia和EL模型的EES所需的EL功能,将始终无法将歧义提及与这些EES正确链接,因为它没有其EL功能。为了解决这个问题,在本文中,我们专注于以一般方式为新兴实体学习EL功能的新任务。我们提出了一种名为Stamo的新颖方法,可以自动学习EES的高质量EL功能,该功能仅需要从网络中收集的每个EE的少数标记文档,因为它可以进一步利用隐藏在未标记的数据中的知识。 Stamo主要基于自我训练,这使其与任何EL功能或EL模型都灵活地集成在一起,但也使其很容易遭受由错误标签的数据引起的错误加强问题。我们认为自我训练是相对于EES的EL特征,而不是一些试图将错误标签的数据抛弃的常见自我训练策略,而是提出了内部插槽和斜率优化的多重优化过程,以减轻误差加强问题隐含。我们构建了涉及选定的EE的两个EL数据集,以评估EES获得的EL特征的质量,实验结果表明,我们的方法显着优于其他学习EL特征的基线方法。
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实体链接旨在将模棱两可的提及与知识库中的相应实体联系起来,这对于各种下游应用程序是重要的,例如知识库完成,问题答案和信息提取。尽管已经致力于这项任务,但这些研究中的大多数遵循以下假设,即可以使用大规模标记的数据。但是,当由于劳动密集型注释工作而导致的标记数据不足以针对特定领域时,现有算法的性能将遭受无法忍受的下降。在本文中,我们努力解决了几个弹药实体链接的问题,这只需要最少的标记数据,并且在实际情况下更为实用。具体而言,我们首先提出了一种新颖的弱监督策略,以基于提及的重写生成非平凡的合成实体对。由于合成数据的质量对有效的模型训练有关键的影响,因此我们进一步设计了一种元学习机制,以自动为每个合成实体对分配不同的权重。通过这种方式,我们可以深刻利用丰富而宝贵的语义信息,从而在几个射击设置下得出训练有素的实体链接模型。现实世界数据集上的实验表明,所提出的方法可以广泛改善最新的几杆实体链接模型,并在只有少量标记的数据可用时实现令人印象深刻的性能。此外,我们还展示了模型可传递性的出色能力。
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在过去的十年中,许多深入学习模型都受到了良好的培训,并在各种机器智能领域取得了巨大成功,特别是对于计算机视觉和自然语言处理。为了更好地利用这些训练有素的模型在域内或跨域转移学习情况下,提出了知识蒸馏(KD)和域适应(DA)并成为研究亮点。他们旨在通过原始培训数据从训练有素的模型转移有用的信息。但是,由于隐私,版权或机密性,原始数据并不总是可用的。最近,无数据知识转移范式吸引了吸引人的关注,因为它涉及从训练有素的模型中蒸馏宝贵的知识,而无需访问培训数据。特别是,它主要包括无数据知识蒸馏(DFKD)和源无数据域适应(SFDA)。一方面,DFKD旨在将域名域内知识从一个麻烦的教师网络转移到一个紧凑的学生网络,以进行模型压缩和有效推论。另一方面,SFDA的目标是重用存储在训练有素的源模型中的跨域知识并将其调整为目标域。在本文中,我们对知识蒸馏和无监督域适应的视角提供了全面的数据知识转移,以帮助读者更好地了解目前的研究状况和想法。分别简要审查了这两个领域的应用和挑战。此外,我们对未来研究的主题提供了一些见解。
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与2D车道相比,实际3D车道数据很难准确收集。在本文中,我们提出了一种仅使用2D车道标签训练3D车道的新方法,称为弱监督的3D车道检测WS-3D车道。通过在相邻车道上的恒定车道宽度和相等高度的假设,我们间接监督训练中的3D车道高度。为了克服数据收集过程中相机音调动态变化的问题,提出了相机音调自校准方法。在锚固表示中,我们提出了一个具有改进的非限量抑制(NMS)方法的双层锚,该方法使基于锚的方法可以预测两条接近的车道线。实验是在两种监督方法下在3D-LANENEN的基础上进行的。在弱监督的环境下,我们的WS-3D车道的表现优于先前的3D-LANEN:APOLLO 3D合成数据集的F得分上升到92.3%,而F1在3DDLANES上上升到74.5%。同时,在纯监督环境中的WS-3D车道可以提高更多的增量,并且优于最先进的设置。据我们所知,WS-3D车道是在弱监督环境下进行3D车道检测的第一次尝试。
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In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.
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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.
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