The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB.

The rapid development and application of natural language generation (NLG) techniques has revolutionized the field of automatic text production. However, these techniques are still limited in their ability to produce human-like text that is truly reasonable and informative. In this paper, we explore the importance of NLG being guided by knowledge, in order to convey human-like reasoning through language generation. We propose ten goals for intelligent NLG systems to pursue, and briefly review the achievement of NLG techniques guided by knowledge and reasoning. We also conclude by envisioning future directions and challenges in the pursuit of these goals.

Given a possibly false claim sentence, how can we automatically correct it with minimal editing? Existing methods either require a large number of pairs of false and corrected claims for supervised training or do not handle well errors spanning over multiple tokens within an utterance. In this paper, we propose VENCE, a novel method for factual error correction (FEC) with minimal edits. VENCE formulates the FEC problem as iterative sampling editing actions with respect to a target density function. We carefully design the target function with predicted truthfulness scores from an offline trained fact verification model. VENCE samples the most probable editing positions based on back-calculated gradients of the truthfulness score concerning input tokens and the editing actions using a distantly-supervised language model (T5). Experiments on a public dataset show that VENCE improves the well-adopted SARI metric by 5.3 (or a relative improvement of 11.8%) over the previous best distantly-supervised methods.

当前的NLP技术已在不同的域中极大地应用。在本文中，我们提出了一个在杂乱无章的场景中机器人抓握的人类框架，调查了掌握过程的语言接口，该框架使用户可以通过自然语言命令进行干预。该框架是在最先进的ras基线基线上构建的，在那里我们使用bert代替场景图表代表场景的文本表示。对模拟和物理机器人的实验表明，所提出的方法在文献中优于基于对象敏捷和场景图的常规方法。此外，我们发现，通过人类干预，绩效可以大大提高。

对话中的情感认可（ERC）旨在检测给定对话中每种话语的情感。新提出的ERC模型利用了预培训的语言模型（PLM），并具有预训练和微调的范式，以获得良好的性能。但是，这些模型很少利用PLM的优势，并且对于缺乏明确的情感表达的对话而表现不佳。为了充分利用与话语中情感表达相关的潜在知识，我们提出了一种新颖的ERC模型Cisper，并使用新的及时和语言模型（LM）调整范式提出。具体而言，Cisper配备了及时融合与对话者的话语相关的上下文信息和常识，以更有效地实现ERC。我们的广泛实验表明，Cisper在最新的ERC模型中的出色表现以及利用这两种重要及时及时提高信息的有效性。为了方便地重现我们的实验结果，Cisper的Sourcecode和数据集已在https://github.com/deqingyang/cisper上共享。

知识嵌入（KE）通过将实体和关系嵌入连续的向量空间来表示知识图（kg）。现有方法主要基于结构或基于描述。基于结构的方法学习保留KGS固有结构的表示。它们不能很好地代表具有有限结构信息的现实世界中的丰富长尾实体。基于描述的方法利用文本信息和语言模型。朝这个方向迈出的先前方法几乎不能胜过基于结构的结构，并且遇到了昂贵的负面抽样和限制性描述需求等问题。在本文中，我们提出了LMKE，该LMKE采用语言模型来得出知识嵌入，旨在既富集了长尾实体的表示形式又旨在解决先前的基于描述的方法的问题。我们通过对比度学习框架制定基于描述的KE学习，以提高培训和评估的效率。实验结果表明，LMKE在链接预测和三重分类的KE基准上实现了最先进的性能，尤其是对于长尾实体。

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.

Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.

神经MWP求解器很难处理小型本地差异。在MWP任务中，一些本地更改节省原始语义，而其他本地更改可能完全更改底层逻辑。目前，MWP任务的现有数据集包含有限的样本，这些样本是神经模型的关键，用于学会消除问题的不同类型的差异并正确解决问题。在本文中，我们提出了一套新型数据增强方法，可以通过不同类型的局部差异增强此类数据来补充现有数据集，并有助于提高当前神经模型的泛化能力。新样本由知识导向实体替换，逻辑引导问题重组产生。确保增强方法保持新数据与其标签之间的一致性。实验结果表明了我们方法的必要性和有效性。

创建什么故事需要推理关于先前陈述以及变更条件的可能结果。人们可以在新条件下轻松生成连贯的结局，但目前系统会对原始故事进行最小的变化来挑战。因此，一个主要挑战是生成逻辑故事和用最小编辑重写之间的权衡。在本文中，我们提出了教育，这是一种基于编辑的无预测方法，用于反复重写。教育包括基于估计在线条件的因果效果的目标位置检测策略，这使故事的因果不变部分。 Bowat然后在流畅，一致性和最小编辑约束下生成故事。我们还提出了一种新的指标来缓解当前自动指标的缺点，更好地评估权衡。我们评估公共反事故事重写基准的教育。实验表明，教育根据自动和人类评估，达到了无监督的SOTA方法的最佳权衡。教育资源可用于：https：//github.com/jiangjiechen/educat。