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近年来,知识图谱问答在医疗、金融、政务等领域被广泛应用。用户不再满足于关于实体属性的单跳问答,而是更多地倾向表达复杂的多跳问答需求。为了应对上述复杂多跳问答,各种不同类型的推理方法被陆续提出。系统地介绍了基于嵌入、路径、逻辑的多跳知识问答推理的最新研究进展以及相关数据集和评测指标,并重点围绕前沿问题进行了讨论。最后总结了现有方法的不足,并展望了未来的研究方向。

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Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy with four perspectives. Next, we describe how to adapt the knowledge of PTMs to the downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

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Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy with four perspectives. Next, we describe how to adapt the knowledge of PTMs to the downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

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