我们引入了翻译误差校正（TEC），这是自动校正人类生成的翻译的任务。机器翻译（MT）的瑕疵具有长期的动机系统，可以通过自动编辑后改善变化后的转换。相比之下，尽管人类直觉上犯了不同的错误，但很少有人注意自动纠正人类翻译的问题，从错别字到翻译约定的矛盾之处。为了调查这一点，我们使用三个TEC数据集构建和释放ACED语料库。我们表明，与自动后编辑数据集中的MT错误相比，TEC中的人类错误表现出更加多样化的错误，翻译流利性误差要少得多，这表明需要专门用于纠正人类错误的专用TEC模型。我们表明，基于人类错误的合成错误的预训练可将TEC F-SCORE提高多达5.1点。我们通过九名专业翻译编辑进行了人类的用户研究，发现我们的TEC系统的帮助使他们产生了更高质量的修订翻译。

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.

自动编辑（APE）旨在通过自动纠正机器翻译输出中的错误来减少手动后编辑工作。由于人类注销的培训数据数量有限，数据稀缺是所有猿类系统所面临的主要挑战之一。为了减轻缺乏真正的培训数据，当前的大多数猿类系统采用数据增强方法来生成大规模的人工语料库。鉴于APE数据增强的重要性，我们分别研究了人工语料库的构建方法和人工数据域对猿类模型性能的影响。此外，猿类的难度在不同的机器翻译（MT）系统之间有所不同。我们在困难的猿数据集上研究了最先进的APE模型的输出，以分析现有的APE系统中的问题。首先，我们发现1）具有高质量源文本和机器翻译文本的人工语料库更有效地改善了猿类模型的性能； 2）内域人工训练数据可以更好地改善猿类模型的性能，而无关紧要的外域数据实际上会干扰该模型； 3）现有的APE模型与包含长源文本或高质量机器翻译文本的案例斗争； 4）最先进的猿类模型在语法和语义添加问题上很好地工作，但是输出容易出现实体和语义遗漏误差。

As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of such metrics at the segment level. Specifically, it is important to investigate metric behaviour when facing accuracy errors in MT because these can have dangerous consequences in certain contexts (e.g., legal, medical). We curate ACES, a translation accuracy challenge set, consisting of 68 phenomena ranging from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge. We use ACES to evaluate a wide range of MT metrics including the submissions to the WMT 2022 metrics shared task and perform several analyses leading to general recommendations for metric developers. We recommend: a) combining metrics with different strengths, b) developing metrics that give more weight to the source and less to surface-level overlap with the reference and c) explicitly modelling additional language-specific information beyond what is available via multilingual embeddings.

由于当前语法纠错（GEC）任务中缺乏并行数据，基于序列框架的模型不能充分培训以获得更高的性能。我们提出了两个数据合成方法，可以控制误差率和合成数据对误差类型的比率。第一种方法是用固定概率损坏单声道语料库中的每个单词，包括更换，插入和删除。另一种方法是培训误差生成模型并进一步过滤模型的解码结果。对不同合成数据的实验表明，误差率为40％，误差类型的比率相同，可以提高模型性能。最后，我们综合了大约1亿数据并实现了与现有技术的可比性，它使用了我们使用的两倍。

The word alignment task, despite its prominence in the era of statistical machine translation (SMT), is niche and under-explored today. In this two-part tutorial, we argue for the continued relevance for word alignment. The first part provides a historical background to word alignment as a core component of the traditional SMT pipeline. We zero-in on GIZA++, an unsupervised, statistical word aligner with surprising longevity. Jumping forward to the era of neural machine translation (NMT), we show how insights from word alignment inspired the attention mechanism fundamental to present-day NMT. The second part shifts to a survey approach. We cover neural word aligners, showing the slow but steady progress towards surpassing GIZA++ performance. Finally, we cover the present-day applications of word alignment, from cross-lingual annotation projection, to improving translation.

在本文中，我们分享了我们努力建立能够翻译一千多种语言的实用机器翻译（MT）系统的发现。我们在三个研究领域中描述了结果：（i）通过利用半监督预训练的语言识别和开发数据驱动的过滤技术来构建1500多种语言的清洁，网挖数据集； （ii）通过利用大规模的多语言模型来开发用于服务不足的语言的实用MT模型，该模型训练了有监督的并行数据，以使用100多种高资源语言和单语言数据集，以增加1000多种语言； （iii）研究这些语言的评估指标的局限性，并对我们MT模型的输出进行定性分析，突出显示了这些类型模型的几种频繁误差模式。我们希望我们的工作为旨在为当前研究的语言构建MT系统的从业者提供有用的见解，并突出显示可以补充Data-Sparse设置中大量多语言模型的弱点的研究方向。

This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.

We report the result of the first edition of the WMT shared task on Translation Suggestion (TS). The task aims to provide alternatives for specific words or phrases given the entire documents generated by machine translation (MT). It consists two sub-tasks, namely, the naive translation suggestion and translation suggestion with hints. The main difference is that some hints are provided in sub-task two, therefore, it is easier for the model to generate more accurate suggestions. For sub-task one, we provide the corpus for the language pairs English-German and English-Chinese. And only English-Chinese corpus is provided for the sub-task two. We received 92 submissions from 5 participating teams in sub-task one and 6 submissions for the sub-task 2, most of them covering all of the translation directions. We used the automatic metric BLEU for evaluating the performance of each submission.

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.

由于它们的低准确性，透明度缺乏透明度，而不是语义，而不是语义，而不是语言技能，而不是语义，而且与人类质量评估的普遍挑剔，机器翻译的传统自动评估度量被语言学家被广泛批评。 MQM样记录形式的人类评估始终是客户和翻译服务提供商（TSP）的真实行业环境中进行的。然而，传统的人类翻译质量评估昂贵才能实现和进入伟大的语言细节，提出对帧间可靠性（IRR）的问题，并且不设计用于衡量比优质质量翻译更糟糕的质量。在这项工作中，我们介绍了希望，基于专业后编辑注释的机器翻译输出的主导和以人为际的评估框架。它仅包含有限数量的常见错误类型，并使用评分模型与错误惩罚点（EPP）的几何进度反映了每个转换单元的错误严重性级别。来自高技术域的英语语言对MT输出的初始实验工作来自高技术领域的营销内容类型的文本揭示了我们的评估框架在反映了关于整体系统级性能和段级透明度的MT输出质量方面非常有效，并且它会增加错误类型解释。该方法具有若干关键优势，例如测量和比较少于不同系统的完美MT输出的能力，表明人类对质量的能力，立即估算所需的劳动力估算，使MT输出到优质的质量，低成本和更快的应用，以及更高的IRR。我们的实验数据可用于\ url {https://github.com/lhan87/hope}。

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective . mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.

自动编辑后（APE）是减少通过机器翻译（MT）系统或软件辅助翻译产生的原始翻译文本错误的重要补救措施。在本文中，我们提出了一种系统的方法来解决越南人的APE任务。具体来说，我们构建了5M越南翻译和纠正句对的第一个大规模数据集。然后，我们使用由构造的数据集应用强大的神经MT模型来处理APE任务。自动和人类评估的实验结果表明了神经MT模型在处理越南APE任务方面的有效性。

机器翻译历史上的重要突破之一是变压器模型的发展。不仅对于各种翻译任务，而且对于大多数其他NLP任务都是革命性的。在本文中，我们针对一个基于变压器的系统，该系统能够将德语用源句子转换为其英语的对应目标句子。我们对WMT'13数据集的新闻评论德语 - 英语并行句子进行实验。此外，我们研究了来自IWSLT'16数据集的培训中包含其他通用域数据以改善变压器模型性能的效果。我们发现，在培训中包括IWSLT'16数据集，有助于在WMT'13数据集的测试集中获得2个BLEU得分点。引入定性分析以分析通用域数据的使用如何有助于提高产生的翻译句子的质量。

最近的神经机翻译研究探索了灵活的发行订单，作为左右一代的替代品。然而，培训非单调模型带来了新的并发症：如何在同一最终结果到达的订单组合爆炸时搜索良好的订单？此外，这些自动排序如何与人类翻译的实际行为进行比较？目前的模型依靠手动构建的偏见或留下自己的所有可能性。在本文中，我们分析了人工后编辑所产生的排序，并使用它们培训自动编辑后系统。我们将生成的系统与由左右和随机编辑排序训练的人进行比较。我们观察到人类倾向于遵循几乎左右的顺序，而是有趣的偏差，例如首选通过纠正标点符号或动词而开始。

Current Machine Translation (MT) models still struggle with more challenging input, such as noisy data and tail-end words and phrases. Several works have addressed this robustness issue by identifying specific categories of noise and variation then tuning models to perform better on them. An important yet under-studied category involves minor variations in nuance (non-typos) that preserve meaning w.r.t. the target language. We introduce and formalize this category as Natural Asemantic Variation (NAV) and investigate it in the context of MT robustness. We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations. We also show that NAV robustness can be transferred across languages and find that synthetic perturbations can achieve some but not all of the benefits of organic NAV data.

MINED BITEXTS可以包含不完美的翻译，从而产生神经机翻译（NMT）的不可靠的训练信号。在已知过滤这样的对以提高最终模型质量的情况下，我们认为它在低资源条件下是次优的，甚至开采数据可以限制。在我们的工作中，我们提出了通过自动编辑来改进挖掘的BIESTS：给出语言XF中的句子，而且可能是IT XE的不完美翻译，我们的模型生成了一个修订的版本XF'或XE'，产生更等值翻译对（即<XF，XE'或<XF'，XE>）。我们使用一个简单的编辑策略（1）挖掘在给定的BITExt中的每个句子的潜在不完美的翻译，（2）学习一个模型来重建原始翻译并以多任务方式翻译。实验表明，我们的方法在大多数情况下，在大多数情况下，我们的方法成功地提高了5个低资源语言对和10个翻译方向，在大多数情况下改善了竞争反播基线。

Pre-training is an effective technique for ensuring robust performance on a variety of machine learning tasks. It typically depends on large-scale crawled corpora that can result in toxic or biased models. Such data can also be problematic with respect to copyright, attribution, and privacy. Pre-training with synthetic tasks and data is a promising way of alleviating such concerns since no real-world information is ingested by the model. Our goal in this paper is to understand what makes for a good pre-trained model when using synthetic resources. We answer this question in the context of neural machine translation by considering two novel approaches to translation model pre-training. Our first approach studies the effect of pre-training on obfuscated data derived from a parallel corpus by mapping words to a vocabulary of 'nonsense' tokens. Our second approach explores the effect of pre-training on procedurally generated synthetic parallel data that does not depend on any real human language corpus. Our empirical evaluation on multiple language pairs shows that, to a surprising degree, the benefits of pre-training can be realized even with obfuscated or purely synthetic parallel data. In our analysis, we consider the extent to which obfuscated and synthetic pre-training techniques can be used to mitigate the issue of hallucinated model toxicity.

合并个人喜好对于高级机器翻译任务至关重要。尽管机器翻译最近进步，但正确反映个人风格仍然是一项艰巨的任务。在本文中，我们引入了一个个性化的自动后编辑框架来应对这一挑战，该挑战有效地产生了考虑不同个人行为的句子。为了构建此框架，我们首先收集后编辑数据，该数据表示来自Live Machine Translation系统的用户偏好。具体而言，现实世界的用户输入源句子进行翻译，并根据用户的首选样式编辑机器翻译的输出。然后，我们提出了一个模型，该模型结合了APE框架上的歧视器模块和特定于用户的参数。实验结果表明，该方法的表现优于四个不同指标（即BLEU，TER，YISI-1和人类评估）的其他基线模型。

在任何翻译工作流程中，从源到目标的域知识保存至关重要。在翻译行业中，接收高度专业化的项目是很常见的，那里几乎没有任何平行的内域数据。在这种情况下，没有足够的内域数据来微调机器翻译（MT）模型，生成与相关上下文一致的翻译很具有挑战性。在这项工作中，我们提出了一种新颖的方法，用于域适应性，以利用最新的审计语言模型（LMS）来用于特定于域的MT的域数据增强，并模拟（a）的（a）小型双语数据集的域特征，或（b）要翻译的单语源文本。将这个想法与反翻译相结合，我们可以为两种用例生成大量的合成双语内域数据。为了进行调查，我们使用最先进的变压器体系结构。我们采用混合的微调来训练模型，从而显着改善了内域文本的翻译。更具体地说，在这两种情况下，我们提出的方法分别在阿拉伯语到英语对阿拉伯语言对上分别提高了大约5-6个BLEU和2-3 BLEU。此外，人类评估的结果证实了自动评估结果。