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。

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。

我们介绍了双图：一种简单但有效的训练策略，以提高神经机器翻译（NMT）性能。它由两个程序组成：双向预处理和单向填充。这两个过程均使用SIMCUT，这是一种简单的正则化方法，迫使原始句子对的输出分布之间的一致性。在不利用额外的数据集通过反翻译或集成大规模预认证的模型的情况下，BI-Simcut可以在五个翻译基准（数据尺寸从160K到20.20万）中实现强大的翻译性能：EN-的BLEU得分为31.16，EN-> DE和38.37的BLEU得分为38.37 de-> en在IWSLT14数据集上，en-> de的30.78和35.15在WMT14数据集上进行DE-> en，而WMT17数据集中的ZH-> EN为27.17。 Simcut不是一种新方法，而是简化和适用于NMT的cutoff（Shen等，2020）的版本，可以将其视为基于扰动的方法。鉴于Simcut和Bi-Simcut的普遍性和简单性，我们认为它们可以作为未来NMT研究的强大基准。

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

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。

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.

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。

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.

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。

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。

由于其误差传播，延迟较少和更少的参数较少的潜力，端到端语音到文本翻译〜（e2e-st）变得越来越受欢迎。鉴于三联培训语料库$ \ langle演讲，转录，翻译\ rangle $，传统的高质量E2E-ST系统利用$ \ langle演讲，转录\ rangle $配对预先培训模型，然后利用$ \ Langle演讲，翻译\ rangle $配对进一步优化它。然而，该过程仅涉及每个阶段的两个元组数据，并且该松散耦合不能完全利用三重态数据之间的关联。在本文中，我们试图基于语音输入模拟转录和翻译的联合概率，以直接利用这种三重态数据。基于此，我们提出了一种新的正规化方法，用于改进三重态数据中双路分解协议的模型培训，理论上应该是相等的。为实现这一目标，我们将两个Kullback-Leibler发散正规化术语介绍到模型培训目的中，以减少双路径输出概率之间的不匹配。然后，训练有素的模型可以通过预定义的早期停止标签自然地被视为E2E-ST模型。 Must-C基准测试的实验表明，我们所提出的方法在所有8个语言对上显着优于最先进的E2E-ST基线，同时在自动语音识别任务中实现更好的性能。我们的代码在https://github.com/duyichao/e2e -st-tda开放。

Pre-trained language models have been successful in natural language generation (NLG) tasks. While various decoding methods have been employed, they often produce suboptimal results. We first present an empirical analysis of three NLG tasks: summarization, machine translation, and constrained text generation. We found that selecting the best output from the results of multiple decoding methods can significantly improve performance. To further improve reranking for NLG tasks, we proposed a novel method, \textsc{PairReranker}, which uses a single encoder and a pairwise loss function to jointly encode a source input and a pair of candidates and compare them. Experiments on three NLG tasks demonstrated the effectiveness and flexibility of \textsc{PairReranker}, showing strong results, compared with previous baselines. In addition, our \textsc{PairReranker} can generalize to significantly improve GPT-3 (text-davinci-003) results (e.g., 24.55\% on CommonGen and 11.35\% on WMT18 zh-en), even though our rerankers are not trained with any GPT-3 candidates.

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.

非自动性变压器（NAT）是文本生成模型的家族，旨在通过并行预测整个句子来减少解码延迟。但是，这种延迟减少牺牲了捕获从左到右的依赖性的能力，从而使NAT学习非常具有挑战性。在本文中，我们介绍了理论和经验分析，以揭示NAT学习的挑战，并提出统一的观点来了解现有的成功。首先，我们表明，简单地通过最大化可能性来训练NAT可以导致边际分布的近似值，但在代币之间降低了所有依赖关系，在该数据集的条件总相关性可以测量删除的信息。其次，我们在统一的框架中正式化了许多以前的目标，并表明他们的成功可以得出结论，以最大程度地提高代理分布的可能性，从而减少了信息损失。实证研究表明，我们的观点可以解释NAT学习中的现象，并指导新培训方法的设计。

通常使用自回归生成模型，尤其是对于涉及顺序数据的那些任务。然而，由于链式有条件建模的内在特征（例如，暴露偏见或缺乏远距离连贯性），由于许多固有的缺陷而困扰着它们，严重限制了它们正确模型分布的能力。在本文中，我们提出了一种独特的方法，该方法称为训练自回旋生成模型，以利用精心设计的基于能量的学习目标。通过利用SoftMax操作的额外自由度，我们被允许使自回归模型本身成为基于能量的模型，用于衡量输入的可能性，而无需引入任何额外的参数。此外，我们表明可以有效地训练电子臂，并能够减轻暴露偏置问题并增加自回归生成模型的时间连贯性。广泛的经验结果涵盖了语言建模，神经机器翻译和图像产生等基准，证明了拟议方法的有效性。

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.

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.

神经文本生成模型可能遭受低多样性问题。已经提出了各种解码策略和基于培训的方法仅通过利用上下文特征来促进多样性，但很少他们考虑纳入句法结构线索。在这项工作中，我们建议使用语言注释，即演讲（POS），来指导文本生成。详细地，我们将POS引导SoftMax介绍以显式模拟两个后部概率：（i）下一页POS，（ii）来自目标POS的词汇的下一个令牌。进一步提出POS导游采样策略来解决POS的多样性来解决低多样性问题。广泛的实验和人类评估表明，与现有最先进的方法相比，我们的POS引导的Softmax和采样（POSG）可以在保持相当的质量的同时产生更多样化的文本。

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by ( 1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new stateof-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.