Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose T-STAR: a model comprising of two components, text-to-AMR encoder and a AMR-to-text decoder. We propose several modeling improvements to enhance the style agnosticity of the generated AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an intermediate representation for TST. With thorough experimental evaluation we show T-STAR significantly outperforms state of the art techniques by achieving on an average 15.2% higher content preservation with negligible loss (3% approx.) in style accuracy. Through detailed human evaluation with 90,000 ratings, we also show that T-STAR has up to 50% lesser hallucinations compared to state of the art TST models.

近年来，文本的风格特性吸引了计算语言学研究人员。具体来说，研究人员研究了文本样式转移（TST）任务，该任务旨在在保留其样式独立内容的同时改变文本的风格属性。在过去的几年中，已经开发了许多新颖的TST算法，而该行业利用这些算法来实现令人兴奋的TST应用程序。由于这种共生，TST研究领域迅速发展。本文旨在对有关文本样式转移的最新研究工作进行全面审查。更具体地说，我们创建了一种分类法来组织TST模型，并提供有关最新技术状况的全面摘要。我们回顾了针对TST任务的现有评估方法，并进行了大规模的可重复性研究，我们在两个公开可用的数据集上实验基准了19个最先进的TST TST算法。最后，我们扩展了当前趋势，并就TST领域的新开发发展提供了新的观点。

文本样式传输是自然语言生成中的重要任务，旨在控制生成的文本中的某些属性，例如礼貌，情感，幽默和许多其他特性。它在自然语言处理领域拥有悠久的历史，最近由于深神经模型带来的有希望的性能而重大关注。在本文中，我们对神经文本转移的研究进行了系统调查，自2017年首次神经文本转移工作以来跨越100多个代表文章。我们讨论了任务制定，现有数据集和子任务，评估，以及丰富的方法在存在并行和非平行数据存在下。我们还提供关于这项任务未来发展的各种重要主题的讨论。我们的策据纸张列表在https://github.com/zhijing-jin/text_style_transfer_survey

非平行文本样式转移是自然语言生成的重要任务。但是，先前的研究集中在令牌或句子级别上，例如句子情绪和形式转移，但在话语水平上忽略了长时间的转移。长文本通常涉及更复杂的作者语言偏好，例如话语结构，而不是句子。在本文中，我们制定了非并行故事作者风格转移的任务，该任务需要将输入故事传输到指定的作者样式的同时，同时维护源语义。为了解决这个问题，我们提出了一个名为StoryTrans的一代模型，该模型利用话语表示捕获源内容信息并将其传输到具有可学习样式嵌入的目标样式中。我们使用额外的培训目标将文学的文学特征与学习的话语表示，以防止模型退化为自动编码器。此外，为了增强内容保存，我们设计了一个面具和填充框架，以将源文本的特定于特定于样式的关键字定为生成。此外，我们分别用中文和英语构建了此任务的新数据集。广泛的实验表明，我们的模型在样式传输和内容保存的总体性能方面优于强大的基线。

我们提出了两种小型无监督方法，用于消除文本中的毒性。我们的第一个方法结合了最近的两个想法：（1）使用小型条件语言模型的生成过程的指导和（2）使用释义模型进行风格传输。我们使用良好的令人措辞的令人愉快的释放器，由风格培训的语言模型引导，以保持文本内容并消除毒性。我们的第二种方法使用BERT用他们的非攻击性同义词取代毒性单词。我们通过使BERT替换具有可变数量的单词的屏蔽令牌来使该方法更灵活。最后，我们介绍了毒性去除任务的风格转移模型的第一个大规模比较研究。我们将模型与许多用于样式传输的方法进行比较。使用无监督的样式传输指标的组合以可参考方式评估该模型。两种方法都建议产生新的SOTA结果。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

Authorship style transfer involves altering the style of text to match the style of some target author whilst preserving the semantic meaning of the original text. Existing approaches to unsupervised authorship style transfer like STRAP have largely focused on style transfer for target authors with many examples of their writing style through books, speeches, or other published works (Krishna et al., 2020). Due to this high-resource training data requirement (often greater than 100,000 words), these approaches are often only useful for style transfer to the style of published authors, politicians, or other well-known figures and authorship styles. In this paper, we attempt to perform low-resource authorship style transfer, a more challenging class of authorship style transfer where only a limited amount of text in the target author's style may exist. In our experiments, we specifically choose source and target authors from Reddit to perform style transfer over their Reddit posts, limiting ourselves to just 16 posts (on average $\approx$ 500 words) of the target author's style. We then propose a method for automatic evaluation on the low-resource authorship style transfer task utilizing authorship and style representation embeddings (Rivera-Soto et al., 2021; Wegmann et al., 2022). We evaluate our style transferred outputs with the proposed automatic evaluation method and find that our method, STYLL, is able to outperform STRAP and a comprehensive set of baselines.

惯用表达式（IES）在自然语言中起重要作用。在本文中，我们研究了惯用句子解释（ISP）的任务，旨在通过用IE用文字解释来解释一个句子。缺乏与惯用语文平行句子的大型语料库是这项任务的主要挑战，我们考虑了两个单独的解决方案。首先，我们向ISP提出了一个无人监督的方法，它利用IE的上下文信息和定义，不需要并行句子训练集。其次，我们提出了一种弱监督的方法，使用后翻来的方法与IE共同执行释义和生成句子，以扩大小规模并行句子训练数据集。该研究的其他重要衍生物包括一种模型，该模型将句子中的文字短语替换为一种与IE生成惯用表达式和具有惯用/文字句对的大规模并行数据集。拟议的解决方案与竞争性基线相比的有效性在Bleu超过5.16点的相对增益中观察到超过8.75点，在使用自动和手动的并行数据集上经验上验证生成的句子时，Sari超过19.57点评估。我们展示了ISP作为EN-DE机器翻译中的预处理步骤的实用实用性。

专家员工的文字式传输技术有可能改善科学社区成员与公众之间的沟通。专家制作的高质量信息往往充满了困难的术语外国人，努力了解。这是医疗领域的一个特别值得注意的问题，其中Layman经常在线医学文本混淆。目前，两个瓶颈干扰了建立高质量医学专家外延式转移系统的目标：曾经专家和外行术语的缺点是普及的预押医学域语言模型，缺乏并行的Corpora培训转让任务本身。为了缓解第一个问题，我们提出了一种新颖的语言模型（LM）预测任务，知识基础同化，从自我监督学习期间将来自专家和外行式医学术语术语的边缘的预先训练数据综合为LM的LM。 。要缓解第二个问题，我们使用基于边缘的标准在医学专家 - Layman域中建立大规模并行语料库。我们的实验表明，基于变压器的模型，以知识库同化和其他良好的预先预订任务对我们的新并行语料库进行了微调，这导致专家外部转账基准的相当大，达到了我们人类评估的平均相对改善总体成功率（OSR），达106％。我们释放我们的代码和并行语料库以供未来的研究。

尽管最近的抽象摘要有所改善，但大多数当前方法都会产生与源文档不一致的摘要，从而严重限制了其在现实世界应用中的信任和使用。最近的作品显示了使用文本或依赖性弧形识别事实错误识别的有希望的改进；但是，他们不会同时考虑整个语义图。为此，我们提出了Factgraph，该方法将文档分解为结构化含义表示（MR），更适合于事实评估。太太描述了核心语义概念及其关系，以规范形式汇总文档和摘要中的主要内容，并减少数据稀疏性。 Factgraph使用与结构感知适配器增强的图形编码器编码此类图，以根据图形连接性捕获概念之间的交互，以及使用基于适配器的文本编码器的文本表示。在不同基准上进行评估事实的实验表明，事实图的表现优于先前的方法高达15％。此外，Factgraph改善了识别内容可验证性错误的性能，并更好地捕获了附近级别的事实不一致。

查询聚焦的文本摘要（QFTS）任务旨在构建基于给定查询的文本文档摘要的构建系统。解决此任务的关键挑战是缺乏培训摘要模型的大量标记数据。在本文中，我们通过探索一系列域适应技术来解决这一挑战。鉴于最近在广泛的自然语言处理任务中进行预先接受的变压器模型的成功，我们利用此类模型为单文档和多文件方案的QFTS任务产生抽象摘要。对于域适应，我们使用预先训练的变压器的摘要模型应用了各种技术，包括转移学习，弱监督学习和远程监督。六个数据集的广泛实验表明，我们所提出的方法非常有效地为QFTS任务产生抽象摘要，同时在一组自动和人类评估指标上设置新的最先进的结果。

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.

Neural machine translation(NMT) has aroused wide attention due to its impressive quality. Beyond quality, controlling translation styles is also an important demand for many languages. Previous related studies mainly focus on controlling formality and gain some improvements. However, they still face two challenges. The first is the evaluation limitation. Style contains abundant information including lexis, syntax, etc. But only formality is well studied. The second is the heavy reliance on iterative fine-tuning when new styles are required. Correspondingly, this paper contributes in terms of the benchmark and approach. First, we re-visit this task and propose a multiway stylized machine translation (MSMT) benchmark, which includes multiple categories of styles in four language directions to push the boundary of this task. Second, we propose a method named style activation prompt (StyleAP) by retrieving prompts from stylized monolingual corpus, which needs no extra fine-tuning. Experiments show that StyleAP could effectively control the style of translation and achieve remarkable performance. All of our data and code are released at https://github.com/IvanWang0730/StyleAP.

数据饥饿的深度神经网络已经将自己作为许多NLP任务的标准建立为包括传统序列标记的标准。尽管他们在高资源语言上表现最先进的表现，但它们仍然落后于低资源场景的统计计数器。一个方法来反击攻击此问题是文本增强，即，从现有数据生成新的合成训练数据点。虽然NLP最近目睹了一种文本增强技术的负载，但该领域仍然缺乏对多种语言和序列标记任务的系统性能分析。为了填补这一差距，我们调查了三类文本增强方法，其在语法（例如，裁剪子句子），令牌（例如，随机字插入）和字符（例如，字符交换）级别上执行更改。我们系统地将它们与语音标记，依赖解析和语义角色标记的分组进行了比较，用于使用各种模型的各种语言系列，包括依赖于诸如MBERT的普赖金的多语言语境化语言模型的架构。增强最显着改善了解析，然后是语音标记和语义角色标记的依赖性解析。我们发现实验技术通常在形态上丰富的语言，而不是越南语等分析语言。我们的研究结果表明，增强技术可以进一步改善基于MBERT的强基线。我们将字符级方法标识为最常见的表演者，而同义词替换和语法增强仪提供不一致的改进。最后，我们讨论了最大依赖于任务，语言对和模型类型的结果。

使用样式转移模型来降低社交媒体评论的侵犯性可以帮助促进更具包容性的环境。但是，没有大量的数据集包含令人反感的文本及其不利的同行，并且具有有限标记数据的微调预审计模型可以导致样式传递文本中原始含义的丧失。为了解决这个问题，我们提供了两个主要贡献。首先，我们发布了第一个公开可用的，平行的反击红色评论及其风格转让的评论，由专家社会语言学家注释。然后，我们介绍了第一个话语感知的样式转移模型，这些模型可以有效地降低Reddit文本中的进攻性，同时保留原始文本的含义。这些模型是第一个检查评论与文本之间回复的推论链接的模型，以转移进攻性reddit文本的样式。我们提出了两种不同的方法，将话语关系与预验证的变压器模型集成在一起，并在我们的Reddit及其无罪分子同行的进攻评论的数据集中对其进行评估。相对于自动指标和人类评估的基线的改进表明，与最先进的话语 - 不可思议的模型相比，我们的话语感知模型在保持样式转移文本的含义方面更好。

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

我们研究了检查问题的事实，旨在识别给定索赔的真实性。具体而言，我们专注于事实提取和验证（发烧）及其伴随数据集的任务。该任务包括从维基百科检索相关文件（和句子）并验证文件中的信息是否支持或驳斥所索赔的索赔。此任务至关重要，可以是假新闻检测和医疗索赔验证等应用程序块。在本文中，我们以通过以结构化和全面的方式呈现文献来更好地了解任务的挑战。我们通过分析不同方法的技术视角并讨论发热数据集的性能结果，描述了所提出的方法，这是最熟悉的和正式结构化的数据集，就是事实提取和验证任务。我们还迄今为止迄今为止确定句子检索组件的有益损失函数的最大实验研究。我们的分析表明，采样负句对于提高性能并降低计算复杂性很重要。最后，我们描述了开放的问题和未来的挑战，我们激励了未来的任务研究。

Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.