Data Augmentation (DA) is frequently used to automatically provide additional training data without extra human annotation. However, data augmentation may introduce noisy data that impairs training. To guarantee the quality of augmented data, existing methods either assume no noise exists in the augmented data and adopt consistency training or use simple heuristics such as training loss and diversity constraints to filter out ``noisy'' data. However, those filtered examples may still contain useful information, and dropping them completely causes loss of supervision signals. In this paper, based on the assumption that the original dataset is cleaner than the augmented data, we propose an on-the-fly denoising technique for data augmentation that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original data. A simple self-regularization module is applied to force the model prediction to be consistent across two distinct dropouts to further prevent overfitting on noisy labels. Our method can be applied to augmentation techniques in general and can consistently improve the performance on both text classification and question-answering tasks.

聊天旨在跨越不同域的人类对话，例如普通的Chit-Chat，知识交流和角色接地对话。为了衡量此类会话代理人的质量，预计对话评估员也会在域中进行评估。但是，大多数最先进的自动对话评估指标（ADMS）不是用于多域评估的。我们有动力设计一般和强大的框架MDD-eval，解决问题。具体而言，我们首先将教师评估员与人类注释的数据一起培训，获取评级技能，以便在特定领域的坏人中讲述良好的对话响应，然后采取自我培训策略，以培训具有教师注释的新评估员的新评估人员域数据，有助于新评估程序遍历多个域。 MDD-EVAL在六个对话评估基准上进行了广泛评估。经验结果表明，在所有评估基准的平均矛盾的普通相关评分方面，MDD-ex律师框架在最先进的adms方面取得了强大的表现，绝对改善了7％。

用于预培训语言模型的自我监督学习的核心包括预训练任务设计以及适当的数据增强。语言模型中的大多数数据增强都是独立于上下文的。最近在电子中提出了一个开创性的增强，并通过引入辅助生成网络（发电机）来实现最先进的性能，以产生用于培训主要辨别网络（鉴别者）的上下文化数据增强。然而，这种设计引入了发电机的额外计算成本，并且需要调整发电机和鉴别器之间的相对能力。在本文中，我们提出了一种自增强策略（SAS），其中单个网络用于审视以后的时期的培训常规预训练和上下文化数据增强。基本上，该策略消除了单独的发电机，并使用单个网络共同执行具有MLM（屏蔽语言建模）和RTD（替换令牌检测）头的两个预训练任务。它避免了寻找适当大小的发电机的挑战，这对于在电子中证明的性能至关重要，以及其随后的变体模型至关重要。此外，SAS是一项常规策略，可以与最近或将来的许多新技术无缝地结合，例如杜伯塔省的解除关注机制。我们的实验表明，SAS能够在具有相似或更少的计算成本中优于胶水任务中的电磁和其他最先进的模型。

除了使用硬标签的标准监督学习外，通常在许多监督学习设置中使用辅助损失来改善模型的概括。例如，知识蒸馏增加了第二个教师模仿模型训练的损失，在该培训中，教师可能是一个验证的模型，可以输出比标签更丰富的分布。同样，在标记数据有限的设置中，弱标记信息以标签函数的形式使用。此处引入辅助损失来对抗标签函数，这些功能可能是基于嘈杂的规则的真实标签近似值。我们解决了学习以原则性方式结合这些损失的问题。我们介绍AMAL，该AMAL使用元学习在验证度量上学习实例特定的权重，以实现损失的最佳混合。在许多知识蒸馏和规则降解域中进行的实验表明，Amal在这些领域中对竞争基准的增长可显着。我们通过经验分析我们的方法，并分享有关其提供性能提升的机制的见解。

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such counterfactual data often has a limited scale and diversity if crowdsourced and is computationally expensive to extend to new perturbation types if generated using supervised methods. To address this, we introduce a new framework called DISCO for automatically generating high-quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters the generation to distill high-quality counterfactual data. We show that learning with this counterfactual data yields a comparatively small student model that is 6% (absolute) more robust and generalizes 5% better across distributions than baselines on various challenging evaluations. This model is also 15% more sensitive in differentiating original and counterfactual examples, on three evaluation sets written by human workers and via human-AI collaboration.

数据增强是通过转换为机器学习的人工创建数据的人工创建，是一个跨机器学习学科的研究领域。尽管它对于增加模型的概括功能很有用，但它还可以解决许多其他挑战和问题，从克服有限的培训数据到正规化目标到限制用于保护隐私的数据的数量。基于对数据扩展的目标和应用的精确描述以及现有作品的分类法，该调查涉及用于文本分类的数据增强方法，并旨在为研究人员和从业者提供简洁而全面的概述。我们将100多种方法划分为12种不同的分组，并提供最先进的参考文献来阐述哪种方法可以通过将它们相互关联，从而阐述了哪种方法。最后，提供可能构成未来工作的基础的研究观点。

半监督学习（SSL）在许多应用领域中已经取得了成功，但这种成功经常涉及任务特定的未标记数据的可用性。知识蒸馏（KD）能够有效地优化紧凑的神经网络，当通过新鲜任务特定的未标记数据蒸馏昂贵的网络时，实现了最佳结果。但是，任务特定的未标记数据可能具有挑战性，特别是对于NLP。我们调查使用生成模型在合成未标记数据中的使用，并呈现一个名为“生成，注释和学习（GAL）”的简单和一般框架。语言模型（LM）用于扫描域中的未标记数据。然后，分类器用于注释这样的数据。最后，综合生成和注释的数据用于推进SSL，KD和NLP和表格任务的几次拍摄学习。为了获得强大的任务特定的LM，我们要么微调来自特定任务的输入的大LM，或者提示具有少数输入示例的大型LM，并且有条件地生成更明显的示例。它还为胶水排行榜上的6层变压器产生了一种新的最先进的。最后，使用GAL的自我训练从UCI存储库的四个表格任务上提供大的收益。

Given the success with in-context learning of large pre-trained language models, we introduce in-context learning distillation to transfer in-context few-shot learning ability from large models to smaller models. We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models. We perform in-context learning distillation under two different few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask In-context Tuning (Multitask-ICT). Multitask-ICT performs better on multitask few-shot learning but also requires more computation than Meta-ICT. Our method shows consistent improvements for both Meta-ICT and Multitask-ICT on two benchmarks: LAMA and CrossFit. Our extensive experiments and analysis reveal that in-context learning objectives and language modeling objectives are complementary under the Multitask-ICT paradigm. In-context learning objectives achieve the best performance when combined with language modeling objectives.

作为有效的策略，数据增强（DA）减轻了深度学习技术可能失败的数据稀缺方案。它广泛应用于计算机视觉，然后引入自然语言处理并实现了许多任务的改进。DA方法的主要重点之一是提高培训数据的多样性，从而帮助模型更好地推广到看不见的测试数据。在本调查中，我们根据增强数据的多样性，将DA方法框架为三类，包括释义，注释和采样。我们的论文根据上述类别，详细分析了DA方法。此外，我们还在NLP任务中介绍了他们的应用以及挑战。

本文提出了一种简单但有效的基于插值的数据增强方法，称为Doublemix，以改善模型在文本分类中的鲁棒性。 Doublemix首先利用几个简单的增强操作来为每个培训数据生成几个扰动的样本，然后使用扰动的数据和原始数据在神经模型的隐藏空间中进行两步插值。具体而言，它首先将扰动的数据混合到合成样本中，然后混合原始数据和合成的扰动数据。 Doublemix通过学习隐藏空间中的“转移”功能来增强模型的鲁棒性。在六个文本分类基准数据集上，我们的方法优于几种流行的文本增强方法，包括令牌级别，句子级别和隐藏级数据增强技术。此外，低资源设置中的实验表明，当培训数据稀缺时，我们的方法一致地改善了模型的性能。广泛的消融研究和案例研究证实，我们方法的每个组成部分都有助于最终表现，并表明我们的方法在具有挑战性的反例中表现出卓越的表现。此外，视觉分析表明，我们方法生成的文本特征是高度可解释的。我们的本文代码可以在https://github.com/declare-lab/doublemix.git上找到。

由于许多微调预先训练的语言模型〜（PLMS）具有有希望的性能，因此慷慨地释放，研究了重用这些模型的更好方法至关重要，因为它可以大大降低再培训计算成本和潜在的环境副作用。在本文中，我们探索了一种小型模型重用范式，知识合并〜（ka）。如果没有人为注释，KA旨在将来自不同教师的知识合并到一个专门从事不同的分类问题中的知识，进入多功能的学生模型。实现这一目标，我们设计了模型不确定感知知识合并〜（Muka）框架，其使用Monte-Carlo辍学来识别潜在的足够教师，以估计金色监督指导学生。实验结果表明，Muka在基准数据集上实现了对基准的基本改进。进一步的分析表明，Muka可以通过多个教师模型，异构教师，甚至交叉数据集教师概括很好的复杂设置。

尽管与专家标签相比，众包平台通常用于收集用于培训机器学习模型的数据集，尽管标签不正确。有两种常见的策略来管理这种噪音的影响。第一个涉及汇总冗余注释，但以较少的例子为代价。其次，先前的作品还考虑使用整个注释预算来标记尽可能多的示例，然后应用Denoising算法来隐式清洁数据集。我们找到了一个中间立场，并提出了一种方法，该方法保留了一小部分注释，以明确清理高度可能的错误样本以优化注释过程。特别是，我们分配了标签预算的很大一部分，以形成用于训练模型的初始数据集。然后，该模型用于确定最有可能是不正确的特定示例，我们将剩余预算用于重新标记。在三个模型变化和四个自然语言处理任务上进行的实验表明，当分配相同的有限注释预算时，旨在处理嘈杂标签的标签聚合和高级denoising方法均优于标签聚合或匹配。

Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability. However, current methods still heavily rely on human efforts or task-specific designs to generate counterfactuals, thereby impeding CAD's applicability to a broad range of NLU tasks. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework. AutoCAD first leverages a classifier to unsupervisedly identify rationales as spans to be intervened, which disentangles spurious and causal features. Then, AutoCAD performs controllable generation enhanced by unlikelihood training to produce diverse counterfactuals. Extensive evaluations on multiple out-of-domain and challenge benchmarks demonstrate that AutoCAD consistently and significantly boosts the out-of-distribution performance of powerful pre-trained models across different NLU tasks, which is comparable or even better than previous state-of-the-art human-in-the-loop or task-specific CAD methods. The code is publicly available at https://github.com/thu-coai/AutoCAD.

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large scale expensive human-annotated datasets. Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotation by leveraging unlabeled data for training. However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging. In this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI where we use a conditional language model, BART to generate the hypotheses for the unlabeled sentences (used as premises). Our experiments show that our SSL framework successfully exploits unlabeled data and substantially improves the performance of four NLI datasets in low-resource settings. We release our code at: https://github.com/msadat3/SSL_for_NLI.

缺乏标记数据是关系提取的主要障碍。通过将未标记的样本作为额外培训数据注释，已经证明，半监督联系提取（SSRE）已被证明是一个有希望的方法。沿着这条线几乎所有先前的研究采用多种模型来使注释通过从这些模型中获取交叉路口集的预测结果来更加可靠。然而，差异集包含有关未标记数据的丰富信息，并通过事先研究忽略了忽视。在本文中，我们建议不仅从共识中学习，而且还要学习SSRE中不同模型之间的分歧。为此，我们开发了一种简单且一般的多教师蒸馏（MTD）框架，可以轻松集成到任何现有的SSRE方法中。具体来说，我们首先让教师对应多个模型，并在SSRE方法中选择最后一次迭代的交叉点集中的样本，以便像往常一样增加标记的数据。然后，我们将类分布转移为差异设置为软标签以指导学生。我们最后使用训练有素的学生模型进行预测。两个公共数据集上的实验结果表明，我们的框架显着促进了基础SSRE方法的性能，具有相当低的计算成本。

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar response variable has been rarely studied, and there exists no KD method applicable to both classification and regression tasks yet. Moreover, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on cGANs, termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 show that we can combine state of the art KD methods with the cGAN-KD framework to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable.

Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resourcerestricted devices. To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large "teacher" BERT can be effectively transferred to a small "student" Tiny-BERT. Then, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pretraining and task-specific learning stages. This framework ensures that TinyBERT can capture the general-domain as well as the task-specific knowledge in BERT. TinyBERT 41 with 4 layers is empirically effective and achieves more than 96.8% the performance of its teacher BERT BASE on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT 4 is also significantly better than 4-layer state-of-the-art baselines on BERT distillation, with only ∼28% parameters and ∼31% inference time of them. Moreover, TinyBERT 6 with 6 layers performs on-par with its teacher BERT BASE .

对比学习被出现为强大的代表学习方法，促进各种下游任务，特别是当监督数据有限时。如何通过数据增强构建有效的对比样本是其成功的关键。与视觉任务不同，语言任务中尚未对对比学习进行对比学习的数据增强方法。在本文中，我们提出了一种使用文本摘要构建语言任务的对比样本的新方法。我们使用这些样本进行监督的对比学习，以获得更好的文本表示，这极大地利用了具有有限注释的文本分类任务。为了进一步改进该方法，除了交叉熵损失之外，我们将从不同类中的样本混合并添加一个名为MIXSUM的额外正则化。真实世界文本分类数据集（Amazon-5，Yelp-5，AG新闻和IMDB）的实验展示了基于摘要的数据增强和MIXSUM正规化的提议对比学习框架的有效性。

致致辞问题答案（CQA）旨在测试模型是否可以回答有关每个人都知道的勤杂朗语言的问题。结合外部知识库的事先作品已经显示了有希望的结果，但知识库是昂贵的构造，并且通常限于固定的一组关系。在本文中，我们专注于更好地利用\ Texit {隐式知识}存储在预先接受预先接受的语言模型中。虽然研究人员发现嵌入在预先接受预先训练的语言模型中的知识，但可以通过填写仔细设计的提取和文本分类的谨慎设计的空白来提取，但如果我们可以在输入和输入的CQA中采用此范例，仍然不清楚输出采取更灵活的形式。为此，我们调查了四种翻译方法，可以将自然问题转化为渗出风格的句子，从语言模型中更好地征求致辞知识，包括基于句法的模型，无监督的神经模型和两个监督的神经模型。此外，要结合不同的翻译方法，我们建议鼓励模型预测与未标记数据不同翻译问题的一致性。我们展示了我们在零拍摄设置中三个CQA数据集上的方法的有效性。我们表明，我们的方法与知识库改进的模型互补，并结合它们可以导致最先进的零射击性能。分析还揭示了不同的强化翻译方法的明显特征，并为什么结合它们导致巨大改进提供了洞察。