无向神经序列模型实现了与最先进的定向序列模型竞争的性能，这些序列模型在机器翻译任务中从左到右单调。在这项工作中，我们培训一项政策，该政策是通过加强学习来学习预先训练的，无向翻译模型的发电顺序。我们表明，通过我们学习的订单解码的翻译可以实现比从左到右解码的输出量更高的BLEU分数或由来自Mansimov等人的学习顺序解码的输出。 （2019）关于WMT'14德语翻译任务。从De-Zh，WMT'16英语 - 罗马尼亚语和WMT'21英语翻译任务的最大来源和目标长度为30的示例，我们的学习订单优于六个任务中的四个启发式生成订单。我们接下来通过定性和定量分析仔细分析学习的订单模式。我们表明我们的政策通常遵循外部到内部顺序，首先预测最左右的位置，然后向中间移动，同时在开始时跳过不太重要的单词。此外，该政策通常在连续步骤中预测单个语法构成结构的位置。我们相信我们的调查结果可以对无向生成模型的机制提供更多的见解，并鼓励在这方面进一步研究。我们的代码在HTTPS://github.com/jiangyctarheel/undirectect - generation

自回归（AR）和非自动增加（NAR）模型对性能和延迟具有自己的优势，将它们与一个模型相结合，可能会利用两者。目前的组合框架更多地关注多个解码范例的集成，具有统一的生成模型，例如，屏蔽语言模型。然而，由于训练目标和推理之间的差距，概括可能对性能有害。在本文中，我们的目标是通过在统一框架下保留AR和NAR的原始目标来缩小差距。具体地，我们通过将AR和NAR共同建模（左右，左右和直）与新引入的方向变量来提出定向变压器（Diformer），这通过控制每个的预测令牌在那方面有特定的依赖关系。通过方向实现的统一成功地保留了AR和NAR中使用的原始依赖性假设，保留了泛化和性能。 4 WMT基准测试的实验表明，Diformer优于当前的联合建模工作，适用于AR和NAR解码的1.5个以上的BLEU积分，也对最先进的独立AR和NAR模型具有竞争力。

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。

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。

在几乎所有文本生成应用中，Word序列在左右（L2R）或左右（R2L）方式中构造，因为自然语言句子是写入L2R或R2L。但是，我们发现自然语言书面订单对文本生成至关重要。在本文中，我们提出了一种螺旋语言建模（SLM），这是一种普遍的方法，使人们能够构建超出L2R和R2L订单的自然语言句子。 SLM允许其中一个从结果文本内的任意令牌开始，并在所选的任意令牌中展开REST令牌。它使解码顺序除了语言模型困惑之外的新优化目标，这进一步提高了所生成文本的分集和质量。此外，SLM使得可以通过选择正确的开始令牌来操纵文本构建过程。 SLM还将生成排序引入了额外的正则化，以提高低资源方案中的模型稳健性。 8次广泛研究的神经机翻译（NMT）任务的实验表明，与传统的L2R解码方法相比，SLM高达4.7 BLEU增加。

我们介绍了一种新的分布式策略梯度算法，并表明它在优化机器翻译模型时，在培训稳定性和概括性绩效方面都优于现有的奖励感知培训程序，例如增强，最低风险培训（MRT）和近端政策优化（PPO）。我们称之为MAD的算法（由于在重要性加权计算中使用平均绝对偏差），它分布式数据生成器在Worker节点上每个源句子对多个候选者进行采样，而中心学习者则更新了策略。 MAD取决于两个降低差异策略：（1）一种有条件的奖励归一化方法，可确保每个源句子都具有正面和负面奖励翻译示例，以及（2）一种新的强大重要性加权方案，充当条件性熵正常化器。在各种翻译任务上进行的实验表明，使用MAD算法在使用贪婪的解码和梁搜索时，使用MAD算法学到的策略表现良好，并且学到的政策对训练过程中使用的特定奖励很敏感。

在本文中，我们提出了一种新的生成模型，逐步逐步的去噪AutoEncoder（Sundae），不依赖于自回归模型。类似地与去噪扩散技术，在从随机输入开始并从随机输入开始并每次直到收敛改善它们时，日出施加Sundae。我们提出了一个简单的新改进运算符，它比扩散方法更少迭代，同时在定性地在自然语言数据集上产生更好的样本。Sundae在WMT'14英语到德语翻译任务上实现最先进的结果（非自回归方法），在巨大清洁的常见爬网数据集和Python代码的数据集上对无条件语言建模的良好定性结果来自GitHub。通过在模板中填充任意空白模式，Sundae的非自动增加性质开辟了超出左右提示的可能性。

非自动回旋（NAR）模型的计算能力比自回归模型较少，但牺牲生成质量可以生成句子。先前的研究通过迭代解码解决了这个问题。这项研究建议将最近的邻居用作NAR解码器的初始状态，并迭代编辑。我们提出了一种新颖的培训策略，以了解有关邻居的编辑操作，以改善NAR文本生成。实验结果表明，所提出的方法（邻域）在JRC-ACQUISIE EN-DE DATASET上获得了更高的翻译质量（比香草变压器高1.69点（比香草变压器高1.69点），而解码迭代率较少（少于十分之一）使用最近的邻居翻译。我们还确认了所提出的方法对数据到文本任务（Wikibio）的有效性。此外，所提出的方法在WMT'14 EN-DE数据集上优于NAR基线。我们还报告了建议方法中使用的邻居示例的分析。

最近非自动增加（NAR）机器翻译最近取得了显着的改进，现在优于一些基准测试的自动增加（AR）模型，为AR推断提供有效的替代方案。然而，虽然AR转换通常使用多语言模型来实现，但是从语言之间的转移和改善的服务效率，多语言NAR模型仍然相对未开发。作为一个示例NAR模型和变压器作为半NAR模型，采用连接员时间分类（CTC），我们展示了多语种NAR的全面实证研究。我们在容量限制下对相关语言与负转移之间的积极转移来测试其能力。随着NAR模型需要蒸馏培训套，我们仔细研究双语与多语种教师的影响。最后，我们适合多语言NAR的缩放法，这使得其相对于AR模型的性能随着模型量表的增加而定量。

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.

Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive learning in neural text generation usually lead to inferior performance. In this paper, we analyse the underlying reasons and propose a new Contrastive Neural Text generation framework, CoNT. CoNT addresses bottlenecks that prevent contrastive learning from being widely adopted in generation tasks from three aspects -- the construction of contrastive examples, the choice of the contrastive loss, and the strategy in decoding. We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation. Experimental results show that CoNT clearly outperforms the conventional training framework on all the ten benchmarks with a convincing margin. Especially, CoNT surpasses previous the most competitive contrastive learning method for text generation, by 1.50 BLEU on machine translation and 1.77 ROUGE-1 on summarization, respectively. It achieves new state-of-the-art on summarization, code comment generation (without external data) and data-to-text generation.

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. Span-BERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1 * Equal contribution. 1 Our code and pre-trained models are available at https://github.com/facebookresearch/ SpanBERT.

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。

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.

Pre-trained models have achieved remarkable success in natural language processing (NLP). However, existing pre-training methods underutilize the benefits of language understanding for generation. Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model. Our model, named as GanLM, is trained with two pre-training objectives: replaced token detection and replaced token denoising. Specifically, given masked source sentences, the generator outputs the target distribution and the discriminator predicts whether the target sampled tokens from distribution are incorrect. The target sentence is replaced with misclassified tokens to construct noisy previous context, which is used to generate the gold sentence. In general, both tasks improve the ability of language understanding and generation by selectively using the denoising data. Extensive experiments in language generation benchmarks show that GanLM with the powerful language understanding capability outperforms various strong pre-trained language models (PLMs) and achieves state-of-the-art performance.

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.

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference -sometimes prohibitively so in the case of very large data sets and large models. Several authors have also charged that NMT systems lack robustness, particularly when input sentences contain rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using residual connections as well as attention connections from the decoder network to the encoder. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. To directly optimize the translation BLEU scores, we consider refining the models by using reinforcement learning, but we found that the improvement in the BLEU scores did not reflect in the human evaluation. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.

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.

最近，多模式机器翻译（MMT）的研究激增，其中其他模式（例如图像）用于提高文本系统的翻译质量。这种多模式系统的特殊用途是同时机器翻译的任务，在该任务中，已证明视觉上下文可以补充源句子提供的部分信息，尤其是在翻译的早期阶段。在本文中，我们提出了第一个基于变压器的同时MMT体系结构，该体系结构以前尚未在现场探索过。此外，我们使用辅助监督信号扩展了该模型，该信号使用标记的短语区域比对来指导其视觉注意机制。我们在三个语言方向上进行全面的实验，并使用自动指标和手动检查进行彻底的定量和定性分析。我们的结果表明，（i）监督视觉注意力一致地提高了MMT模型的翻译质量，并且（ii）通过监督损失对MMT进行微调，比从SCRATCH训练MMT的MMT可以提高性能。与最先进的模型相比，我们提出的模型可实现多达2.3 bleu和3.5 Meteor点的改善。

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.