对事件序列的预测对于信息检索和自然语言处理中的许多现实世界应用至关重要。在事件序列预测中，未来的活动生成（FEG）是一项具有挑战性的任务，因为它不仅需要流利的文本生成，而且需要常识性推理才能保持整个事件故事的逻辑连贯性。在本文中，我们提出了一个新颖的可解释的FEG框架COEP。它突出并整合了两种类型的事件知识，对直接事件事件关系的顺序知识以及推论知识，这些知识反映了事件之间的中间角色心理学（例如意图，原因，反应），这些心理本质地将故事推向了故事。为了减轻知识遗忘问题，我们为每种类型的知识设计了两个模块，即IM和GM，它们是通过及时调整组合的。首先，IM专注于理解推论知识，以产生常识性解释并为通用汽车提供软提示向量。我们还设计了一种对比歧视器，以提高概括能力。其次，GM通过用IM的指导对直接顺序知识进行建模来生成未来事件。自动和人类评估表明，我们的方法可以产生更连贯，具体和逻辑的未来事件。

Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement. As such, researchers have long attempted to create systems that can generate stories automatically. In recent years, powered by deep learning and massive data resources, automatic story generation has shown significant advances. However, considerable challenges, like the need for global coherence in generated stories, still hamper generative models from reaching the same storytelling ability as human narrators. To tackle these challenges, many studies seek to inject structured knowledge into the generation process, which is referred to as structure knowledge-enhanced story generation. Incorporating external knowledge can enhance the logical coherence among story events, achieve better knowledge grounding, and alleviate over-generalization and repetition problems in stories. This survey provides the latest and comprehensive review of this research field: (i) we present a systematical taxonomy regarding how existing methods integrate structured knowledge into story generation; (ii) we summarize involved story corpora, structured knowledge datasets, and evaluation metrics; (iii) we give multidimensional insights into the challenges of knowledge-enhanced story generation and cast light on promising directions for future study.

动机，情感和行动是人类活动中相关的基本因素。尽管长期以来一直认为动机和情感是探索人们如何在人类活动中采取行动的核心，但几乎没有研究支持分析人类精神状态与行动之间的关系。我们介绍了第一项研究，该研究研究了基于语言的人类活动中建模动机，情感和行动的生存能力，即逗号（人类活动的认知框架）。在逗号的指导下，我们定义了三个自然语言处理任务（情感理解，动机理解和有条件的动作生成），并通过自动从故事常识中提取样本来建立一个具有挑战性的数据集冰雹。 NLP应用程序的实验结果证明了建模关系的有效性。此外，与现有方法相比，受逗号启发的模型可以更好地揭示动机，情感和行动之间的基本关系。

自动化讲故事长期以来一直抓住了研究人员在日常生活中的叙述中的难以感受。但是，在用神经语言模型产生叙述时，保持一致性并保持对特定结束的特定结束挑战。在本文中，我们介绍了读者模型（Storm）的故事生成，这是一个框架，其中读者模型用于推理故事的推理应该进步。读者模型是人类读者相信关于虚构故事世界的概念，实体和关系的人。我们展示了如何作为知识图表所代表的明确读者模型提供故事一致性，并以实现给定的故事世界目标的形式提供可控性。实验表明，我们的模型产生了显着更加连贯和主题的故事，优于尺寸的基线，包括情节合理性并保持主题。我们的系统也优于在未订购的情况下在组成给定概念时占总引导的故事生成基线。

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.

近年来带来了对自然语言理解领域的勤义代表和推理的重新兴趣。新的致辞知识图表（CSKG）的发展是这些进步的核心，因为他们的不同事实可以通过机器学习模型来解决新的和具有挑战性的任务。与此同时，由于全面地涵盖了一般勤杂朗知识所需的大规模规模，对这些资源的质量和覆盖率仍存在疑问。在这项工作中，我们将手动构建的CSKGS分配在NLP代理商遇到的所有情况下，我们将永远不会实现适用所需的覆盖范围。因此，我们提出了一种新的评估框架，用于测试KGS的效用，基于如何从中学习有效的隐式知识表示。通过这一新目标，我们提出了一个含有知识的全新CSKG的新CSKG，该知识不容易获得预用的语言模型。我们与其他领先的CSKG相比，评估其属性，表现了对勤杂朗语言知识资源的第一个大规模对研究。接下来，我们显示原子2020更适合培训知识模型，可以为新的，看不见的实体和事件产生准确，代表知识。最后，通过人类评估，我们表明，尽管使用超过430倍的参数，但GPT-3（175B参数）的几次射击性能较低，而令人印象深刻，令人印象深刻，令人印象深刻，令人印象深刻，仍然低于原子型2020的巴特的知识模型。

Pre-trained Language Models (PLMs) which are trained on large text corpus through the self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tuning stage, they still have some limitations such as poor reasoning ability due to the lack of external knowledge. Incorporating knowledge into PLMs has been tried to tackle these issues. In this paper, we present a comprehensive review of Knowledge-Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear insight into this thriving field. We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight the focus of these two kinds of tasks. For NLU, we take several types of knowledge into account and divide them into four categories: linguistic knowledge, text knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are categorized into KG-based and retrieval-based methods. Finally, we point out some promising future directions of KE-PLMs.

在本文中，我们建议利用对话的独特特征，共享参与者的常识性知识，以解决总结它们的困难。我们提出了病态的框架，该框架使用常识推论作为其他背景。与以前仅依赖于输入对话的工作相比，Sick使用外部知识模型来生成丰富的常识推断，并选择具有基于相似性选择方法的最可能的推理。基于生病的，病人++的理解为监督，在总结多任务学习环境中的对话时，添加了产生常识推断的任务。实验结果表明，通过注入常识性知识，我们的框架比现有方法产生更多信息和一致的摘要。

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.

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.

人类使用自然语言来撰写普通概念，将他们的环境归结为合理的日常场景描述。然而，这种生成的致辞推理（GCSR）技能缺乏最先进的文本生成方法。关于由神经文本生成模型（例如，预先接受的文本到文本变压器）生成的任意概念的描述性句子通常是语法流畅的，但可能与人类常识不相符，这主要是由于它们缺乏捕获概念关系的机制识别隐式概念，并对看不见的概念组成来执行概括的推理。在本文中，我们提出了一种想象的 - 言语（I＆V）方法，其学会在输入概念之间的关系中想象一个关系场景知识图（SKG），并在生成合理的场景描述时利用SKG作为约束。我们收集和协调来自不同领域和方式的一套知识资源，为I＆v提供丰富的辅助监督信号。该实验展示了I＆V在提高概念到句子和概念到故事的生成任务上的语言模型的有效性，同时使模型能够从更少的任务示例中学习并生成对人类注入者常识的SKG。

神经桌面到文本的生成方法是渴望数据的，限制了它们对低资源现实世界应用的适应性。先前的工作主要诉诸于训练的语言模型（PLM），以生成表格的表格摘要。但是，由于PLM的性质不受控制，它们通常包含幻觉内容。此外，很少研究表和序列之间的拓扑差异。最后但并非最不重要的一点是，在PLM上进行少量实例进行微调可能会导致过度贴合和灾难性的遗忘。为了减轻这些问题，我们提出了一种基于及时的方法，前缀控制的发电机（即PCG），用于几乎没有表格到文本的生成。我们为PLM的特定于任务的前缀预备，以使表结构更适合预训练的输入。此外，我们生成一个特定于输入的前缀，以控制生成的文本的事实内容和单词顺序。对Wikibio数据集的不同领域（人类，书籍和歌曲）的自动评估和人类评估都显示出对基线方法的实质性改进。

尽管在产生流利的文本方面取得了进步，但现有的预训练模型倾向于在产生诸如故事和新闻之类的叙述时将不连贯的事件序列附加到相关实体上。我们猜想，这些问题是由将实体表示为浅表词的静态嵌入而导致的，同时忽略了对其不断变化的状态建模，即随着文本的展开，即它们所携带的信息。因此，我们将变压器模型扩展到动态执行实体状态更新和叙事生成的句子实现。我们提出了一个对比框架，以在离散空间中学习状态表示，并将其他注意层插入解码器中以更好地利用这些状态。两个叙述数据集的实验表明，与有意义的实体状态的指导相比，我们的模型可以产生更多的连贯和多样化的叙事。

Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, there is still much room for improvement, especially in terms of the coverage of the given topics and the coherence of the generated text. In this paper, we propose a novel approach called TegFormer which utilizes the Transformer architecture where the encoder is enriched with domain-specific contexts while the decoder is enhanced by a large-scale pre-trained language model. Specifically, a \emph{Topic-Extension} layer capturing the interaction between the given topics and their domain-specific contexts is plugged into the encoder. Since the given topics are usually concise and sparse, such an additional layer can bring more topic-related semantics in to facilitate the subsequent natural language generation. Moreover, an \emph{Embedding-Fusion} module that combines the domain-specific word embeddings learnt from the given corpus and the general-purpose word embeddings provided by a GPT-2 model pre-trained on massive text data is integrated into the decoder. Since GPT-2 is at a much larger scale, it contains a lot more implicit linguistic knowledge which would help the decoder to produce more grammatical and readable text. Extensive experiments have shown that the pieces of text generated by TegFormer have better topic coverage and higher text coherence than those from SOTA topic-to-essay techniques, according to automatic and human evaluations. As revealed by ablation studies, both the Topic-Extension layer and the Embedding-Fusion module contribute substantially to TegFormer's performance advantage.

Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs still face a number of challenges including poor interpretability, weak reasoning capability, and the need for a lot of expensive annotated data when applied to downstream tasks. By integrating external knowledge into PLMs, \textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained \underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to overcome the above-mentioned limitations. In this paper, we examine KEPLMs systematically through a series of studies. Specifically, we outline the common types and different formats of knowledge to be integrated into KEPLMs, detail the existing methods for building and evaluating KEPLMS, present the applications of KEPLMs in downstream tasks, and discuss the future research directions. Researchers will benefit from this survey by gaining a quick and comprehensive overview of the latest developments in this field.

可以利用致辞知识来识别文本中的因果关系。在这项工作中，我们在Atomic2020中言语三元组，广泛的覆盖率致辞推理知识图表，到自然语言文本，并不断预先预留伯特普瑞赖林模型。我们评估了回答勤杂朗语言推理问题所产生的模型。我们的研究结果表明，通过致致通知推理知识增强了不断预付费的语言模型在两个致辞语言推理基准测试，COPA和BCOPA-CE上表现出我们的基线，而无需对基础模型的额外改进或使用质量增强的数据进行微调。

深度神经语言模型的最新进展与大规模数据集的能力相结合，加速了自然语言生成系统的发展，这些系统在多种任务和应用程序上下文中产生流利和连贯的文本（在各种成功程度上）。但是，为所需的用户控制这些模型的输出仍然是一个开放的挑战。这不仅对于自定义生成语言的内容和样式至关重要，而且对于他们在现实世界中的安全可靠部署至关重要。我们提出了一项关于受约束神经语言生成的新兴主题的广泛调查，在该主题中，我们通过区分条件和约束（后者是在输出文本上而不是输入的可检验条件），正式定义和分类自然语言生成问题，目前是可检验的）约束文本生成任务，并查看受限文本生成的现有方法和评估指标。我们的目的是强调这个新兴领域的最新进展和趋势，以告知最有希望的方向和局限性，以推动受约束神经语言生成研究的最新作品。

会话推荐系统（CRS）旨在主动引起用户偏好，并通过自然语言对话推荐高质量的项目。通常，CRS由建议模块组成，以预测用户的首选项目和对话模块，以生成适当的响应。要开发有效的CR，必须无缝整合两个模块。现有作品要么设计语义一致性策略，要么共享两个模块之间的知识资源和表示。但是，这些方法仍然依靠不同的体系结构或技术来开发两个模块，因此很难进行有效的模块集成。为了解决这个问题，我们根据知识增强的及时学习提出了一个名为UNICRS的统一CRS模型。我们的方法将建议和对话子任务统一到及时学习范式中，并根据固定的预训练的语言模型（PLM）利用知识增强的提示来以统一的方法来实现两个子任务。在及时的设计中，我们包括融合的知识表示，特定于任务的软令牌和对话环境，它们可以提供足够的上下文信息以适应CRS任务的PLM。此外，对于建议子任务，我们还将生成的响应模板作为提示的重要组成部分结合起来，以增强两个子任务之间的信息交互。对两个公共CRS数据集进行的广泛实验证明了我们方法的有效性。

大型预先训练的生成语言模型的出现为AI故事的常见框架通过采样模型来创建持续故事的序列。然而，单独的抽样对故事产生不足。特别是，很难指导语言模型来创建故事以达到特定的目标事件。我们提出了两种在深增强学习和奖励塑造的自动化技术，以控制计算机生成的故事的情节。首先利用近端策略优化来微调现有的基于变换器的语言模型，以生成文本持续，而且是寻求目标。第二种提取来自展开故事的知识图，该故事由策略网络使用，具有图注意选择由语言模型生成的候选继续。我们报告了与故事如何实现给定的目标事件以及与基线和消融相比的一致性和整体故事质量的人类参与者排名的自动化指标报告。

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。