虽然已经提出了许多背景感知神经机器转换模型在翻译中包含语境，但大多数模型在句子级别对齐的并行文档上培训结束到底。因为只有少数域（和语言对）具有此类文档级并行数据，所以我们无法在大多数域中执行准确的上下文感知转换。因此，我们通过将文档级语言模型结合到解码器中，提出了一种简单的方法将句子级转换模型转换为上下文感知模型。我们的上下文感知解码器仅在句子级并行语料库和单语演模板上构建;因此，不需要文档级并行数据。在理论上，这项工作的核心部分是使用上下文和当前句子之间的点亮互信息的语境信息的新颖表示。我们以三种语言对，英语到法语，英语到俄语，以及日语到英语，通过评估，通过评估以及对上下文意识翻译的对比测试。

本文概述了NVIDIA Nemo的神经电机翻译系统，用于WMT21新闻和生物医学共享翻译任务的受限数据跟踪。我们的新闻任务提交英语 - 德语（EN-DE）和英语 - 俄语（EN-RU）是基于基于基于基线变换器的序列到序列模型之上。具体而言，我们使用1）检查点平均2）模型缩放3）模型缩放3）与从左右分解模型的逆转传播和知识蒸馏的数据增强4）从前一年的测试集上的FINETUNING 5）型号集合6）浅融合解码变压器语言模型和7）嘈杂的频道重新排名。此外，我们的BioMedical任务提交的英语 - 俄语使用生物学偏见的词汇表，并从事新闻任务数据的划痕，从新闻任务数据集中策划的医学相关文本以及共享任务提供的生物医学数据。我们的新闻系统在WMT'20 en-de试验中实现了39.5的Sacrebleu得分优于去年任务38.8的最佳提交。我们的生物医学任务ru-en和en-ru系统分别在WMT'20生物医学任务测试集中达到43.8和40.3的Bleu分数，优于上一年的最佳提交。

这项工作适用于最低贝叶斯风险（MBR）解码，以优化翻译质量的各种自动化指标。机器翻译中的自动指标最近取得了巨大的进步。特别是，在人类评级（例如BLEurt，或Comet）上微调，在与人类判断的相关性方面是优于表面度量的微调。我们的实验表明，神经翻译模型与神经基于基于神经参考度量，BLEURT的组合导致自动和人类评估的显着改善。通过与经典光束搜索输出不同的翻译获得该改进：这些翻译的可能性较低，并且较少受到Bleu等表面度量的青睐。

Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic backtranslation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English↔German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish→English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English→German.

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.

Reranking methods in machine translation aim to close the gap between common evaluation metrics (e.g. BLEU) and maximum likelihood learning and decoding algorithms. Prior works address this challenge by training models to rerank beam search candidates according to their predicted BLEU scores, building upon large models pretrained on massive monolingual corpora -- a privilege that was never made available to the baseline translation model. In this work, we examine a simple approach for training rerankers to predict translation candidates' BLEU scores without introducing additional data or parameters. Our approach can be used as a clean baseline, decoupled from external factors, for future research in this area.

具有释义生成的长期问题是如何获得可靠的监督信号。在本文中，我们基于假设产生与鉴定相同的上下文相同的含义的两个句子的概率应该是相同的，提出了一种无监督的范例。灵感来自这一基本因的主意，我们提出了一种流水线系统，该系统由基于上下文语言模型的候选候选生成组成，使用评分函数的候选滤波，以及基于所选候选者的释放模型训练。提议的范例提供了现有的释义生成方法的优点：（1）使用上下文规范器在含义上，该模型能够产生大量的高质量释义对; （2）使用人为可解释的评分功能来选择来自候选者的释义对，所提出的框架为开发人员提供了一种与数据生成过程进行干预的通道，导致更可控的模型。不同任务和数据集的实验结果表明，拟议模型在监督和无人监督的设置中的有效性。

本文介绍了一种新的数据增强方法，用于神经机器翻译，该方法可以在语言内部和跨语言内部实施更强的语义一致性。我们的方法基于条件掩盖语言模型（CMLM），该模型是双向的，可以在左右上下文以及标签上有条件。我们证明CMLM是生成上下文依赖性单词分布的好技术。特别是，我们表明CMLM能够通过在替换过程中对源和目标进行调节来实现语义一致性。此外，为了增强多样性，我们将软词替换的想法纳入了数据增强，该概念用词汇上的概率分布代替了一个单词。在不同量表的四个翻译数据集上进行的实验表明，总体解决方案会导致更现实的数据增强和更好的翻译质量。与最新作品相比，我们的方法始终取得了最佳性能，并且在基线上的提高了1.90个BLEU点。

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.

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.

我们假设现有的句子级机器翻译（MT）指标在人类参考包含歧义时会效率降低。为了验证这一假设，我们提出了一种非常简单的方法，用于扩展预审计的指标以在文档级别合并上下文。我们将我们的方法应用于三个流行的指标，即Bertscore，Prism和Comet，以及无参考的公制Comet-QE。我们使用提供的MQM注释评估WMT 2021指标共享任务的扩展指标。我们的结果表明，扩展指标的表现在约85％的测试条件下优于其句子级别的级别，而在排除低质量人类参考的结果时。此外，我们表明我们的文档级扩展大大提高了其对话语现象任务的准确性，从而优于专用基线高达6.1％。我们的实验结果支持我们的初始假设，并表明对指标的简单扩展使他们能够利用上下文来解决参考中的歧义。

Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, there is still a significant performance gap between NMT and SiMT. In this work, we propose to leverage monolingual data to improve SiMT, which trains a SiMT student on the combination of bilingual data and external monolingual data distilled by Seq-KD. Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e.g., +3.15 BLEU on En-Zh). Inspired by the behavior of human simultaneous interpreters, we propose a novel monolingual sampling strategy for SiMT, considering both chunk length and monotonicity. Experimental results show that our sampling strategy consistently outperforms the random sampling strategy (and other conventional typical NMT monolingual sampling strategies) by avoiding the key problem of SiMT -- hallucination, and has better scalability. We achieve +0.72 BLEU improvements on average against random sampling on En-Zh and En-Ja. Data and codes can be found at https://github.com/hexuandeng/Mono4SiMT.

本文介绍了亚当·米基维奇大学（Adam Mickiewicz University）（AMU）提交的《 WMT 2022一般MT任务》的踪迹。我们参加了乌克兰$ \ leftrightarrow $捷克翻译指示。这些系统是基于变压器（大）体系结构的四个模型的加权合奏。模型使用源因素来利用输入中存在的命名实体的信息。合奏中的每个模型仅使用共享任务组织者提供的数据培训。一种嘈杂的反向翻译技术用于增强培训语料库。合奏中的模型之一是文档级模型，该模型在平行和合成的更长序列上训练。在句子级的解码过程中，集合生成了N最佳列表。 n-最佳列表与单个文档级模型生成的n-最佳列表合并，该列表一次翻译了多个句子。最后，使用现有的质量估计模型和最小贝叶斯风险解码来重新列出N最好的列表，因此根据彗星评估指标选择了最佳假设。根据自动评估结果，我们的系统在两个翻译方向上排名第一。

神经自回归序列模型涂抹许多可能​​序列之间的概率，包括退化的序列，例如空或重复序列。在这项工作中，我们解决了一个特定的情况，其中模型为不合理的短序列分配高概率。我们定义了量化速率以量化此问题。在确认神经机翻译中高度过度的过天气后，我们建议明确地减少培训期间的过天平率。我们进行一组实验来研究建议的正规化对模型分布和解码性能的影响。我们使用神经电脑翻译任务作为测试用，并考虑三个不同大小的不同数据集。我们的实验显示了三个主要结果。首先，我们可以通过调整正规化的强度来控制模型的过天平率。其次，通过提高过度损失贡献，令牌的概率和等级在不应该是它的位置下降。第三，所提出的正则化影响光束搜索的结果，特别是当使用大梁时。用大梁的翻译质量（在BLEU中测量）的降解显着减少了较低的过天速速率，但与较小光束尺寸相比的劣化仍有剩余状态。从这些观察中，我们得出结论，高度过度的过度性是神经自回归模型中过于可能的短序列的退化情况背后的主要原因。

Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.

嘈杂的频道模型在神经机翻译（NMT）中特别有效。然而，最近的方法如“波束搜索和重新划分”（BSR）在推理期间引起了大量的计算开销，使实际应用不可行。我们的目标是建立一个摊销嘈杂的频道NMT模型，使得从它贪婪解码将生成转换，以最大化与使用BSR生成的翻译相同的奖励。我们尝试三种方法：知识蒸馏，1阶梯偏差仿制学习和Q学习。第一方法获得来自伪语料库的噪声信道信号，后两种方法旨在直接针对嘈杂的通道MT奖励优化。所有三种级别的速度推动速度推断为1-2级。对于所有三种方法，所生成的翻译无法实现与BSR相当的奖励，但BLEU近似的翻译质量类似于BSR产生的翻译的质量。

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.

机器翻译系统（MTS）是通过将文本或语音从一种语言转换为另一种语言的有效工具。在像印度这样的大型多语言环境中，对有效的翻译系统的需求变得显而易见，英语和一套印度语言（ILS）正式使用。与英语相反，由于语料库的不可用，IL仍然被视为低资源语言。为了解决不对称性质，多语言神经机器翻译（MNMT）系统会发展为在这个方向上的理想方法。在本文中，我们提出了一个MNMT系统，以解决与低资源语言翻译有关的问题。我们的模型包括两个MNMT系统，即用于英语印度（一对多），另一个用于指示英语（多一对多），其中包含15个语言对（30个翻译说明）的共享编码器码头。由于大多数IL对具有很少的平行语料库，因此不足以训练任何机器翻译模型。我们探索各种增强策略，以通过建议的模型提高整体翻译质量。最先进的变压器体系结构用于实现所提出的模型。大量数据的试验揭示了其优越性比常规模型的优势。此外，本文解决了语言关系的使用（在方言，脚本等方面），尤其是关于同一家族的高资源语言在提高低资源语言表现方面的作用。此外，实验结果还表明了ILS的倒退和域适应性的优势，以提高源和目标语言的翻译质量。使用所有这些关键方法，我们提出的模型在评估指标方面比基线模型更有效，即一组ILS的BLEU（双语评估研究）得分。

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。

众所周知，由出色的文档级神经机器翻译（NMT）模型产生的翻译是一致且连贯的。但是，像BLEU这样的现有句子级评估指标几乎无法反映模型在文档级别的性能。为了解决这个问题，我们在本文中提出了一种话语凝聚评估方法（DCOEM），并贡献了一个新的测试套件，该套件考虑了四个凝聚力的方式（参考，连接，替代和词汇凝聚力），以衡量文档翻译的凝聚力。最近的文档级NMT系统的评估结果表明，我们的方法在估计文档级别的翻译方面是实用且至关重要的。