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题目: Integrating Deep Learning with Logic Fusion for Information Extraction

摘要:

信息抽取(Information extraction, IE)旨在从输入文本中产生结构化的信息,例如命名实体识别和关系抽取。通过特征工程或深度学习为IE提出了各种尝试。然而,他们中的大多数人并没有将任务本身所固有的复杂关系联系起来,而这一点已被证明是特别重要的。例如,两个实体之间的关系高度依赖于它们的实体类型。这些依赖关系可以看作是复杂的约束,可以有效地表示为逻辑规则。为了将这种逻辑推理能力与深度神经网络的学习能力相结合,我们提出将一阶逻辑形式的逻辑知识集成到深度学习系统中,以端到端方式联合训练。该集成框架通过逻辑规则对神经输出进行知识正则化增强,同时根据训练数据的特点更新逻辑规则的权值。我们证明了该模型在多个IE任务上的有效性和泛化性。

作者:

Sinno Jialin Pan是南洋理工大学计算机科学与工程学院院长兼副教授,研究方向是迁移学习、数据挖掘、人工智能、机器学习。

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In this paper, we perform an in-depth study of how data augmentation techniques improve synthetic or spoofed audio detection. Specifically, we propose methods to deal with channel variability, different audio compressions, different band-widths, and unseen spoofing attacks, which have all been shown to significantly degrade the performance of audio-based systems and Anti-Spoofing systems. Our results are based on the ASVspoof 2021 challenge, in the Logical Access (LA) and Deep Fake (DF) categories. Our study is Data-Centric, meaning that the models are fixed and we significantly improve the results by making changes in the data. We introduce two forms of data augmentation - compression augmentation for the DF part, compression & channel augmentation for the LA part. In addition, a new type of online data augmentation, SpecAverage, is introduced in which the audio features are masked with their average value in order to improve generalization. Furthermore, we introduce a Log spectrogram feature design that improved the results. Our best single system and fusion scheme both achieve state-of-the-art performance in the DF category, with an EER of 15.46% and 14.46% respectively. Our best system for the LA task reduced the best baseline EER by 50% and the min t-DCF by 16%. Our techniques to deal with spoofed data from a wide variety of distributions can be replicated and can help anti-spoofing and speech-based systems enhance their results.

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