Sockeye 3是神经机器翻译（NMT）的Mockeye工具包的最新版本。现在，基于Pytorch，Sockeye 3提供了更快的模型实现和更高级的功能，并具有进一步的简化代码库。这可以通过更快的迭代，对更强大，更快的模型进行有效的培训以及快速从研究转移到生产的新想法的灵活性，从而实现更广泛的实验。当运行可比较的型号时，Sockeye 3的速度比GPU上的其他Pytorch实现快126％，在CPU上的实现速度高达292％。Sockeye 3是根据Apache 2.0许可发布的开源软件。

多语种NMT已成为MT在生产中部署的有吸引力的解决方案。但是要匹配双语质量，它符合较大且较慢的型号。在这项工作中，我们考虑了几种方法在推理时更快地使多语言NMT变得更快而不会降低其质量。我们在两种20语言多平行设置中尝试几个“光解码器”架构：在TED会谈中小规模和帕拉克曲线上的大规模。我们的实验表明，将具有词汇过滤的浅解码器组合在于，在翻译质量下没有损失的速度超过两倍。我们用Bleu和Chrf（380语言对），鲁棒性评估和人类评估验证了我们的研究结果。

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.

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。

FAIRSEQ is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found here: https://www.youtube. com/watch?v=OtgDdWtHvto.

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。

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.

我们为神经机翻译（NMT）提供了一个开源工具包。新工具包主要基于拱形变压器（Vaswani等，2017）以及下面详述的许多其他改进，以便创建一个独立的，易于使用，一致和全面的各个领域的机器翻译任务框架。它是为了支持双语和多语言翻译任务的工具，从构建各个语料库的模型开始推断新的预测或将模型打包给提供功能的JIT格式。

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

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。

同时翻译，它在仅在源句中只收到几个单词后开始翻译每个句子，在许多情况下都具有重要作用。虽然以前的前缀到前缀框架被认为适合同时翻译并实现良好的性能，但它仍然有两个不可避免的缺点：由于需要为每个延迟的单独模型训练单独模型而导致的高计算资源成本$ k $和不足能够编码信息，因为每个目标令牌只能参加特定的源前缀。我们提出了一种新颖的框架，采用简单但有效的解码策略，该策略专为全句型而设计。在此框架内，培训单个全句型模型可以实现任意给出的延迟并节省计算资源。此外，随着全句型模型的能力来编码整个句子，我们的解码策略可以在实时增强在解码状态中保持的信息。实验结果表明，我们的方法在4个方向上的基准方向达到了更好的翻译质量：Zh $ \ lightarrow $ en，en $ \ lightarrow $ ro和en $ \ leftrightarrow $ de。

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。

随着自然语言处理领域的最新发展，在使用不同架构的神经机翻译中的使用情况上升了。变压器架构用于实现最先进的准确性，但它们是训练的非常昂贵的昂贵。每个人都不能拥有由高端GPU和其他资源组成的等待。我们在低计算资源上培训我们的模型，并调查结果。正如预期的那样，变形金刚表现出其他架构，但结果有一些令人惊讶的结果。由更多编码器和解码器组成的变形金刚需要花更多的时间来训练，但有更少的BLEU分数。LSTM在实验中表现良好，比较少花时间训练而不是变压器，适合在具有时间限制的情况下使用。

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes an encoder, decoder and attention module, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-the-art results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT'14 and WMT'15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.

最先进的编码器模型（例如，用于机器翻译（MT）或语音识别（ASR））作为原子单元构造并端到端训练。没有其他模型的任何组件都无法（重新）使用。我们描述了Legonn，这是一种使用解码器模块构建编码器架构的过程，可以在各种MT和ASR任务中重复使用，而无需进行任何微调。为了实现可重复性，每个编码器和解码器模块之间的界面都基于模型设计器预先定义的离散词汇，将其接地到边缘分布序列。我们提出了两种摄入这些边缘的方法。一个是可区分的，可以使整个网络的梯度流动，另一个是梯度分离的。为了使MT任务之间的解码器模块的可移植性用于不同的源语言和其他任务（例如ASR），我们引入了一种模态不可思议的编码器，该模态编码器由长度控制机制组成，以动态调整编码器的输出长度，以匹配预期的输入长度范围的范围预训练的解码器。我们提出了几项实验来证明Legonn模型的有效性：可以重复使用德国英语（DE-EN）MT任务的训练有素的语言解码器模块，而没有对Europarl English ASR和ROMANIAN-ENGLISH进行微调（RO）（RO）（RO）（RO） -en）MT任务以匹配或击败相应的基线模型。当针对数千个更新的目标任务进行微调时，我们的Legonn模型将RO-EN MT任务提高了1.5个BLEU点，并为Europarl ASR任务降低了12.5％的相对减少。此外，为了显示其可扩展性，我们从三个模块中构成了一个legonn ASR模型 - 每个模块都在三个不同数据集的不同端到端训练的模型中学习 - 将降低的减少降低到19.5％。

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。

基于变压器的神经模型在许多AI应用中使用。培训这些模型很昂贵，因为它需要大量的GPU资源和较长的持续时间。这是具有挑战性的，因为诸如句子之类的典型数据具有可变的长度，而变压器的计算模式比卷积神经网络更为复杂。现有系统要么仅专注于模型推理，要么仅针对BERT样编码器模型进行优化。在本文中，我们提出了LightSeq2，该系统是为GPU上的一般变压器模型加速培训的系统。我们提出了一系列针对变压器模型的特定计算流量和内存访问模式量身定制的GPU优化技术。 LightSeq2支持许多模型体系结构，包括BERT（仅编码），GPT（仅解码器），变压器（编码器编码器）和视觉变压器。我们对各种模型和基准测试的实验表明，LightSeq2始终比不同GPU上的先前系统更快（1.4-3.5倍）。特别是，与大型公共机器翻译基准（WMT14英语 - 德国人）上的现有系统相比，它获得了308％的培训速度。

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.

Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via Transformer from scratch, especially on small datasets usually fails to converge. Our dedicated probing tasks show that 1) both the absolute position and relative position information gets gradually weakened or even vanished once it reaches the upper encoder layers, and 2) the vanishing of absolute position information in encoder output causes the training failure of Doc2Doc NMT. To alleviate this problem, we propose a position-aware Transformer (P-Transformer) to enhance both the absolute and relative position information in both self-attention and cross-attention. Specifically, we integrate absolute positional information, i.e., position embeddings, into the query-key pairs both in self-attention and cross-attention through a simple yet effective addition operation. Moreover, we also integrate relative position encoding in self-attention. The proposed P-Transformer utilizes sinusoidal position encoding and does not require any task-specified position embedding, segment embedding, or attention mechanism. Through the above methods, we build a Doc2Doc NMT model with P-Transformer, which ingests the source document and completely generates the target document in a sequence-to-sequence (seq2seq) way. In addition, P-Transformer can be applied to seq2seq-based document-to-sentence (Doc2Sent) and sentence-to-sentence (Sent2Sent) translation. Extensive experimental results of Doc2Doc NMT show that P-Transformer significantly outperforms strong baselines on widely-used 9 document-level datasets in 7 language pairs, covering small-, middle-, and large-scales, and achieves a new state-of-the-art. Experimentation on discourse phenomena shows that our Doc2Doc NMT models improve the translation quality in both BLEU and discourse coherence. We make our code available on Github.

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. 1 Compared to recurrent models, computations over all elements can be fully parallelized during training to better exploit the GPU hardware and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.