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.

How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data? Pre-trained language models (PLMs) have been effective for a wide range of NLP tasks. However, existing approaches either require fine-tuning on downstream labeled datasets or manually constructing proper prompts. In this paper, we propose nonparametric prompting PLM (NPPrompt) for fully zero-shot language understanding. Unlike previous methods, NPPrompt uses only pre-trained language models and does not require any labeled data or additional raw corpus for further fine-tuning, nor does it rely on humans to construct a comprehensive set of prompt label words. We evaluate NPPrompt against previous major few-shot and zero-shot learning methods on diverse NLP tasks: including text classification, text entailment, similar text retrieval, and paraphrasing. Experimental results demonstrate that our NPPrompt outperforms the previous best fully zero-shot method by big margins, with absolute gains of 12.8% in accuracy on text classification and 18.9% on the GLUE benchmark.

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.

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。

医疗保健提供者通常会记录给每位患者提供临床，研究和计费目的的临床护理的详细说明。由于这些叙述的非结构性性质，提供者使用专门的员工使用国际疾病（ICD）编码系统为患者的诊断分配诊断代码。此手动过程不仅耗时，而且昂贵且容易出错。先前的工作证明了机器学习（ML）方法在自动化此过程中的潜在效用，但它依靠大量手动标记数据来训练模型。此外，诊断编码系统随着时间的流逝而演变，这使得传统的监督学习策略无法推广到本地应用程序之外。在这项工作中，我们引入了一个普遍的弱监督文本分类框架，该框架仅从类标签描述中学习，而无需使用任何人类标记的文档。它利用预先训练的语言模型中存储的语言领域知识和数据编程框架将代码标签分配给单个文本。我们通过将方法与四个现实世界文本分类数据集中的最先进的弱文本分类器进行比较，除了将ICD代码分配给公开可用的模拟MIMIC-III数据库中的医疗注释外，我们证明了我们的方法的功效和灵活性。

我们提出了Patron，这是一种新方法，它使用基于及时的不确定性估计，用于在冷启动场景下进行预训练的语言模型进行微调的数据选择，即，没有初始标记的数据可用。在顾客中，我们设计（1）一种基于迅速的不确定性传播方法来估计数据点的重要性和（2）分区 - 然后 - 剥离（PTR）策略，以促进对注释的样品多样性。六个文本分类数据集的实验表明，赞助人的表现优于最强的冷启动数据选择基准，高达6.9％。此外，仅具有128个标签，顾客分别基于香草微调和及时的学习，获得了91.0％和92.1％的全面监督性能。我们的赞助人实施可在\ url {https://github.com/yueyu1030/patron}上获得。

传统文本分类方法通常需要良好数量的标记数据，这很难获得，尤其是限制域或较少的广泛语言。这种缺乏标记的数据导致了低资源方法的兴起，这在自然语言处理中具有低数据可用性。其中，零射击学习脱颖而出，它包括在没有任何先前标记的数据的情况下学习分类器。通过此方法报告的最佳结果使用变压器等语言模型，但下降到两个问题：高执行时间和无法处理长文本作为输入。本文提出了一种新的模型Zeroberto，它利用无监督的聚类步骤来获得分类任务之前的压缩数据表示。我们展示Zeroberto对长输入和更短的执行时间具有更好的性能，在FOLHauol数据集中的F1分数中表现出XLM-R大约12％。关键词：低资源NLP，未标记的数据，零射击学习，主题建模，变形金刚。

Current state-of-the-art approaches to text classification typically leverage BERT-style Transformer models with a softmax classifier, jointly fine-tuned to predict class labels of a target task. In this paper, we instead propose an alternative training objective in which we learn task-specific embeddings of text: our proposed objective learns embeddings such that all texts that share the same target class label should be close together in the embedding space, while all others should be far apart. This allows us to replace the softmax classifier with a more interpretable k-nearest-neighbor classification approach. In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions. (2) This facilitates qualitative inspection of the training data, helping us to better understand the problem space and identify labelling quality issues. (3) The learned distances to some degree generalize to unseen classes, allowing us to incrementally add new classes without retraining the model. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy, thus pointing to practical applicability of our proposed approach.

Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining indomain (domain-adaptive pretraining) leads to performance gains, under both high-and low-resource settings. Moreover, adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multiphase adaptive pretraining offers large gains in task performance.

社会科学家经常将文本文档分类为使用结果标签作为实证研究的结果或预测指标。自动化文本分类已成为标准工具，因为它需要较少的人体编码。但是，学者们仍然需要许多人类标记的文件来培训自动分类器。为了降低标签成本，我们提出了一种新的文本分类算法，将概率模型与主动学习结合在一起。概率模型同时使用标记和未标记的数据，而主动学习集中在难以分类的文件上标记工作。我们的验证研究表明，我们的算法的分类性能与最先进的方法相当，而计算成本的一部分。此外，我们复制了两篇最近发表的文章，并得出相同的实质性结论，其中仅占这些研究中使用的原始标记数据的一小部分。我们提供ActiveText，一种开源软件来实现我们的方法。

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.

近年来，预制语言模型彻底改变了NLP世界，同时在各种下游任务中实现了最先进的性能。但是，在许多情况下，当标记数据稀缺时，这些模型不会表现良好，并且预计模型将在零或几秒钟内执行。最近，有几项工作表明，与下游任务更好地对准的预先预测或执行第二阶段，可以导致改进的结果，尤其是在稀缺数据设置中。在此，我们建议利用携带的情绪话语标记来产生大规模的弱标记数据，这又可以用于适应语言模型进行情感分析。广泛的实验结果显示了我们在各种基准数据集中的方法的价值，包括金融域。在https://github.com/ibm/tslm-discourse-markers上提供代码，模型和数据。

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents for a given query. We introduce the first large-scale collection of approximately 40 retrieval datasets with instructions, BERRI, and present TART, a multi-task retrieval system trained on BERRI with instructions. TART shows strong capabilities to adapt to a new retrieval task via instructions and advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval to better reflect real-world scenarios, where diverse domains and tasks are pooled and a system needs to find documents aligning users' intents. In this setup, TART significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.

由于表现强劲，预用的语言模型已成为许多NLP任务的标准方法，但他们培训价格昂贵。我们提出了一个简单高效的学习框架TLM，不依赖于大规模预制。给定一些标记的任务数据和大型常规语料库，TLM使用任务数据作为查询来检索一般语料库的微小子集，并联合优化任务目标和从头开始的语言建模目标。在四个域中的八个分类数据集上，TLM实现了比预用语言模型（例如Roberta-Light）更好地或类似的结果，同时减少了两个数量级的训练拖鞋。高精度和效率，我们希望TLM将有助于民主化NLP并加快发展。

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.

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。

Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the performance of text classification varies with context. Given the lack of labelled dataset, we proposed a novel and simple unsupervised text classification model to classify cargo content in international shipping industry using the Standard International Trade Classification (SITC) codes. Our method stems from representing words using pretrained Glove Word Embeddings and finding the most likely label using Cosine Similarity. To compare unsupervised text classification model with supervised classification, we also applied several Transformer models to classify cargo content. Due to lack of training data, the SITC numerical codes and the corresponding textual descriptions were used as training data. A small number of manually labelled cargo content data was used to evaluate the classification performances of the unsupervised classification and the Transformer based supervised classification. The comparison reveals that unsupervised classification significantly outperforms Transformer based supervised classification even after increasing the size of the training dataset by 30%. Lacking training data is a key bottleneck that prohibits deep learning models (such as Transformers) from successful practical applications. Unsupervised classification can provide an alternative efficient and effective method to classify text when there is scarce training data.

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。

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。