Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from users' conversation data. Given some users of a conversational system, a personal attribute and these users' utterances, our goal is to predict the ranking of the given personal attribute values for each user. Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory. In addition, it is found that some text classification methods could be employed to resolve this task directly. However, they also perform not well over those difficult personal attributes. In this paper, we propose a novel framework PEARL to predict personal attributes from conversations by leveraging the abundant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or external data are utilized. PEARL combines the biterm semantic information with the word co-occurrence information seamlessly via employing the updated prior attribute knowledge to refine the biterm topic model's Gibbs sampling process in an iterative manner. The extensive experimental results show that PEARL outperforms all the baseline methods not only on the task of personal attribute prediction from conversations over two data sets, but also on the more general weakly supervised text classification task over one data set.

开放信息提取（OIE）方法从非结构化文本中提取大量的OIE三元<名词短语，关系短语，名词短语>，它们组成了大型开放知识基础（OKB）。此类OKB中的名词短语和关系短语不是规范化的，这导致了散落和冗余的事实。发现知识的两种观点（即，基于事实三重的事实视图和基于事实三重源上下文的上下文视图）提供了互补信息，这对于OKB规范化的任务至关重要，该信息将其簇为同义名词短语和关系短语分为同一组，并为他们分配唯一的标识符。但是，到目前为止，这两种知识的观点已被现有作品孤立地利用。在本文中，我们提出了CMVC，这是一个新颖的无监督框架，该框架利用这两种知识的观点共同将典范的OKBS化，而无需手动注释的标签。为了实现这一目标，我们提出了一种多视图CH K均值聚类算法，以相互加强通过考虑其不同的聚类质量从每个视图中学到的特定视图嵌入的聚类。为了进一步提高规范化的性能，我们在每个特定视图中分别提出了一个培训数据优化策略，以迭代方式完善学习视图的特定嵌入。此外，我们提出了一种对数跳跃算法，以数据驱动的方式预测簇数的最佳数量，而无需任何标签。我们通过针对最新方法的多个现实世界OKB数据集进行了广泛的实验来证明我们的框架的优势。

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed.

我们研究了弱监督的文本分类问题，旨在将文本文档分类为只有类别曲面名称的一组预定义类，而没有提供任何注释的培训文件。大多数现有方法利用每个文档中的文本信息。然而，在许多领域中，文件伴随着各种类型的元数据（例如，作者，场地和研究文件的年份）。除了文本内容之外，这些元数据及其组合可以作为强大的类别指标。在本文中，我们探讨了使用元数据来帮助弱监督文本分类的潜力。具体而言，我们通过异构信息网络模拟文档和元数据之间的关系。为了有效地捕获网络中的高阶结构，我们使用图案来描述元数据组合。我们提出了一个名为Motifclass的新颖框架，（1）选择类别 - 指示性主题实例，（2）根据类别名称和指示性主题实例检索并生成伪标记的训练样本，并且（3）使用文本分类器培训伪培训数据。关于现实世界数据集的广泛实验证明了Motifclass对现有弱监督的文本分类方法的卓越表现。进一步的分析显示了考虑我们框架中的高阶元数据信息的益处。

GitHub已成为代码共享和科学交流的重要平台。使用大量的存储库可用，需要基于主题的搜索需求。即使介绍了主题标签功能，大多数GitHub存储库都没有任何标签，阻碍了搜索和基于主题的分析。这项工作将自动存储库分类问题定位为关键字驱动的分层分类。具体而言，用户只需要提供具有关键字的标签层次结构以作为监控提供。此设置灵活，适用于用户的需求，占主题标签的不同粒度，需要最小的人力努力。我们确定了这个问题的三个关键挑战，即（1）多模态信号的存在; （2）监督稀缺和偏见; （3）监督格式不匹配。为了认识到这些挑战，我们提出了一种HIGITCLASS框架，包括三个模块：异构信息网络嵌入;关键词富集;主题建模和伪文档生成。在两个GitHub存储库集合上的实验结果证实，HIGITCLASS优于现有的弱监督和DATALESS分层分类方法，尤其是集成了用于存储库分类的结构化和非结构化数据的能力。

Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seed-guided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.

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.

在科学研究中，该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展，正在提出，修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息，并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法，为研究任务选择适当的方法并提出新方法。此外，方法实体的演变可以揭示纪律的发展并促进知识发现。因此，本文对方法论和经验作品进行了系统的综述，重点是从全文学术文献中提取方法实体，并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义，我们系统地审查了提取和评估方法实体的方法和指标，重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后，讨论了现有作品的限制以及潜在的下一步。

Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE and SBERT-based baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.

识别异常文档，其内容与语料库中的大多数文档不同，在管理大型文本集合中发挥了重要作用。但是，由于没有关于Inlier（或目标）分布的明确信息，现有的无监督异常探测器可能会根据语料库中的异常值的密度或多样性进行不可靠的结果。为了解决这一挑战，我们介绍了一项新的任务，称为类别无类别检测，该任务旨在通过使用类别名称作为弱监管来将文档与Inlier（或目标）类别的语义相关。在实践中，该任务可以广泛适用于，它可以灵活地根据用户的兴趣指定目标类别的范围，同时仅需要目标类别名称作为最小指导。在本文中，我们介绍了一个类别超类的检测框架，它有效地根据其特定于类别的相关性得分，有效地测量每个文档的一个目标类别之一。我们的框架采用两步方法; （i）它首先通过利用在文本嵌入空间中编码的单词文件相似度，然后（ii）通过使用伪标签来计算伪标签以计算置信度来生成所有未标记的文档的伪类别标签从其目标类别预测。真实世界数据集的实验表明，我们的框架在指定不同目标类别的各种场景中的所有基线方法中实现了最佳检测性能。

旨在为每个文档分配主题标签的文档分类在各种应用程序中扮演基本作用。尽管在传统的监督文件分类中存在现有研究的成功，但它们不太关注两个真正的问题：（1）元数据的存在：在许多域中，文本伴随着作者和标签等各种附加信息。此类元数据充当令人信服的主题指标，应将其利用到分类框架中; （2）标签稀缺性：在某些情况下，标记的训练样本价格昂贵，只需要使用一小组注释数据来执行分类。为了认识到这两个挑战，我们提出了MetaCAT，是一个最小的监督框架，可以用元数据分类文本。具体地，我们开发了一个生成过程，描述了单词，文档，标签和元数据之间的关系。由生成模型引导，我们将文本和元数据嵌入到相同的语义空间中以编码异构信号。然后，基于相同的生成过程，我们综合训练样本来解决标签稀缺的瓶颈。我们对各种数据集进行了彻底的评估。实验结果证明了Metacat在许多竞争基础上的有效性。

在电子商务平台上，预测是否彼此兼容两种产品是获得值得信赖的产品推荐和搜索经验的重要功能。但是，由于异质产品数据以及缺乏手动策划的培训数据，难以准确预测产品兼容性。我们研究发现有效的标签规则的问题，这些规则可以实现弱监督的产品兼容性预测。我们开发了Amrule，这是一个多视图规则发现框架，可以（1）自适应地发现新颖的统治者，可以补充当前的弱监督模型以改善兼容性预测； （2）从结构化属性表和非结构化产品描述中发现可解释的规则。 Amrule通过提升风格的策略从大错误实例中自适应地发现标签规则，高质量的规则可以纠正当前模型的弱点并迭代地完善模型。为了从结构化产品属性发现规则，我们从决策树中生成可合and的高阶规则；对于从非结构化产品描述中发现规则，我们从预先训练的语言模型中生成基于及时的规则。 4个现实世界数据集的实验表明，AMRULE平均比基准的表现高出5.98％，并提高了规则质量和规则建议效率。

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。

Personalized chatbots focus on endowing the chatbots with a consistent personality to behave like real users and further act as personal assistants. Previous studies have explored generating implicit user profiles from the user's dialogue history for building personalized chatbots. However, these studies only use the response generation loss to train the entire model, thus it is prone to suffer from the problem of data sparsity. Besides, they overemphasize the final generated response's quality while ignoring the correlations and fusions between the user's dialogue history, leading to rough data representations and performance degradation. To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots. Specifically, we apply contrastive sampling methods to leverage the supervised signals hidden in user dialog history, and generate the pre-training samples for enhancing the model. We design three pre-training tasks based on three types of contrastive pairs from user dialogue history, namely response pairs, sequence augmentation pairs, and user pairs. We pre-train the utterance encoder and the history encoder towards the contrastive objectives and use these pre-trained encoders for generating user profiles while personalized response generation. Experimental results on two real-world datasets show a significant improvement in our proposed model MCP compared with the existing methods.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

立场检测旨在确定文本的作者是否赞成，反对或中立。这项任务的主要挑战是两个方面的：由于不同目标以及缺乏目标的上下文信息而产生的几乎没有学习。现有作品主要通过设计基于注意力的模型或引入嘈杂的外部知识来解决第二期，而第一个问题仍未探索。在本文中，受到预训练的语言模型（PLM）的潜在能力（PLM）的启发，我们建议介绍基于立场检测的及时基于迅速的微调。 PLM可以为目标提供基本的上下文信息，并通过提示启用几次学习。考虑到目标在立场检测任务中的关键作用，我们设计了目标感知的提示并提出了一种新颖的语言。我们的语言器不会将每个标签映射到具体单词，而是将每个标签映射到矢量，并选择最能捕获姿势与目标之间相关性的标签。此外，为了减轻通过单人工提示来处理不同目标的可能缺陷，我们建议将信息从多个提示中学到的信息提炼。实验结果表明，我们提出的模型在全数据和少数场景中的表现出色。

基于强大的预训练语言模型（PLM）的密集检索方法（DR）方法取得了重大进步，并已成为现代开放域问答系统的关键组成部分。但是，他们需要大量的手动注释才能进行竞争性，这是不可行的。为了解决这个问题，越来越多的研究作品最近着重于在低资源场景下改善DR绩效。这些作品在培训所需的资源和采用各种技术的资源方面有所不同。了解这种差异对于在特定的低资源场景下选择正确的技术至关重要。为了促进这种理解，我们提供了针对低资源DR的主流技术的彻底结构化概述。根据他们所需的资源，我们将技术分为三个主要类别：（1）仅需要文档； （2）需要文件和问题； （3）需要文档和提问对。对于每种技术，我们都会介绍其一般形式算法，突出显示开放的问题和利弊。概述了有希望的方向以供将来的研究。

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. In this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue. DiaASQ bridges the gap between fine-grained sentiment analysis and conversational opinion mining. We manually construct a large-scale, high-quality Chinese dataset and also obtain the English version dataset via manual translation. We deliberately propose a neural model to benchmark the task. It advances in effectively performing end-to-end quadruple prediction and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We finally point out several potential future works to facilitate the follow-up research of this new task. The DiaASQ data is open at https://github.com/unikcc/DiaASQ

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

机器学习方法尤其是深度神经网络取得了巨大的成功，但其中许多往往依赖于一些标记的样品进行训练。在真实世界的应用中，我们经常需要通过例如具有新兴预测目标和昂贵的样本注释的动态上下文来解决样本短缺。因此，低资源学习，旨在学习具有足够资源（特别是培训样本）的强大预测模型，现在正在被广泛调查。在所有低资源学习研究中，许多人更喜欢以知识图（kg）的形式利用一些辅助信息，这对于知识表示变得越来越受欢迎，以减少对标记样本的依赖。在这项调查中，我们非常全面地审查了90美元的报纸关于两个主要的低资源学习设置 - 零射击学习（ZSL）的预测，从未出现过训练，而且很少拍摄的学习（FSL）预测的新类仅具有可用的少量标记样本。我们首先介绍了ZSL和FSL研究中使用的KGS以及现有的和潜在的KG施工解决方案，然后系统地分类和总结了KG感知ZSL和FSL方法，将它们划分为不同的范例，例如基于映射的映射，数据增强，基于传播和基于优化的。我们接下来呈现了不同的应用程序，包括计算机视觉和自然语言处理中的kg增强预测任务，还包括kg完成的任务，以及每个任务的一些典型评估资源。我们最终讨论了一些关于新学习和推理范式的方面的一些挑战和未来方向，以及高质量的KGs的建设。