变压器的令人印象深刻的性能归因于自我注意力，在每个位置都考虑了整个输入之间的依赖性。在这项工作中，我们改革了神经$ n $ gram模型，该模型仅着眼于每个位置的几个周围表示，其多头机制如Vaswani等人（2017年）。通过对序列到序列任务的实验，我们表明，用多头神经$ n $ gram在变压器中替换自我注意力可以比变压器实现可比性或更好的性能。从对我们提出的方法的各种分析中，我们发现多头神经$ n $ gram是互补的，它们的组合可以进一步提高香草变压器的性能。

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。

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.† Work performed while at Google Brain.‡ Work performed while at Google Research.

Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. ( 2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graphlabeled inputs.

现有的文档级神经计算机翻译（NMT）模型具有足够探索的不同上下文设置，为目标生成提供指导。但是，对于慷慨的上下文信息，对揭开更多样化的背景的注意力很少。在本文中，我们提出了一种选择性的内存增强神经文件翻译模型，以处理包含上下文的大假设空间的文档。具体而言，我们从训练语料库中检索类似的双语句子对来增强全局上下文，然后通过选择性机制扩展双流注意模型，以捕获本地上下文和不同的全局背景。这种统一的方法允许我们的模型在三个公开的文档级机器翻译数据集上优雅地培训，并且显着优于以前的文档级NMT型号。

变压器注意机制中的设计选择，包括弱电感偏置和二次计算复杂性，限制了其用于建模长序列的应用。在本文中，我们介绍了一个简单的，理论上的，单头的门控注意机制，配备了（指数）移动平均线，以将局部依赖性的电感偏置纳入位置 - 敏锐的注意机制中。我们进一步提出了一个具有线性时间和空间复杂性的大型变体，但通过将整个序列分为固定长度的多个块，仅产生最小的质量损失。对广泛的序列建模基准测试的广泛实验，包括远距离竞技场，神经机器翻译，自动回归语言建模以及图像和语音分类，表明，巨人比其他序列模型取得了重大改进，包括变种物的变体和最新的变体模型状态空间模型。

事实证明，构象异构体在许多语音处理任务中都是有效的。它结合了使用卷积和使用自我注意的全球依赖性提取本地依赖的好处。受此启发，我们提出了一个更灵活，可解释和可自定义的编码器替代方案，分支机构，并在端到端语音处理中对各种远程依赖关系进行建模。在每个编码器层中，一个分支都采用自我注意事项或其变体来捕获远程依赖性，而另一个分支则利用带有卷积门控（CGMLP）的MLP模块来提取局部关系。我们对几种语音识别和口语理解基准进行实验。结果表明，我们的模型优于变压器和CGMLP。它还与构象异构体获得的最先进结果相匹配。此外，由于两分支结构，我们展示了减少计算的各种策略，包括在单个训练有素的模型中具有可变的推理复杂性的能力。合并分支的权重表明如何在不同层中使用本地和全球依赖性，从而使模型设计受益。

Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads in the encoder to the overall performance of the model and analyze the roles played by them. We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L 0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance. For example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads results in a drop of only 0.15 BLEU. 1

In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step). However, previous work on interpretability in NMT has mainly focused solely on source sentence tokens' attributions. Therefore, we lack a full understanding of the influences of every input token (source sentence and target prefix) in the model predictions. In this work, we propose an interpretability method that tracks input tokens' attributions for both contexts. Our method, which can be extended to any encoder-decoder Transformer-based model, allows us to better comprehend the inner workings of current NMT models. We apply the proposed method to both bilingual and multilingual Transformers and present insights into their behaviour.

变压器注意机制的二次计算和内存复杂性限制了对长序列建模的可扩展性。在本文中，我们提出了Luna，一种线性统一嵌套关注机制，使Softmax注意力具有两个嵌套线性关注功能，仅产生线性（与二次）的时间和空间复杂度相反。具体地，通过第一注意功能，LUNA将输入序列包装成固定长度的序列。然后，使用第二关注功能未包装包装序列。与更传统的关注机制相比，LUNA引入具有固定长度的附加序列作为输入和额外的相应输出，允许LUNA线性地进行关注操作，同时还存储足够的上下文信息。我们对三个序列建模任务的基准进行了广泛的评估：长上下文序列建模，神经机平移和大型预磨损的屏蔽语言建模。竞争甚至更好的实验结果表明了Luna的有效性和效率与各种各样相比

Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions. In particular, multi-headed attention is a driving force behind many recent state-of-the-art natural language processing (NLP) models such as Transformer-based MT models and BERT. These models apply multiple attention mechanisms in parallel, with each attention "head" potentially focusing on different parts of the input, which makes it possible to express sophisticated functions beyond the simple weighted average. In this paper we make the surprising observation that even if models have been trained using multiple heads, in practice, a large percentage of attention heads can be removed at test time without significantly impacting performance. In fact, some layers can even be reduced to a single head. We further examine greedy algorithms for pruning down models, and the potential speed, memory efficiency, and accuracy improvements obtainable therefrom. Finally, we analyze the results with respect to which parts of the model are more reliant on having multiple heads, and provide precursory evidence that training dynamics play a role in the gains provided by multi-head attention 1 .1 Code to replicate our experiments is provided at https://github.com/pmichel31415/ are-16-heads-really-better-than-1

变压器建立在多头缩放的点产生关注和位置编码的基础上，旨在学习特征表示和令牌依赖性。在这项工作中，我们专注于通过学习通过变压器中的自我发项机制来增强特征图来增强独特的表示。具体而言，我们提出了水平的关注，以重新权重降低维度降低的点产量注意的多头输出，并提出垂直注意力以通过对不同的相互依赖性在不同的相互依赖性的方面自适应重新校准的频道响应，以使不同频道。我们证明了配备了两种专注的变压器模型在不同监督的学习任务中具有很高的概括能力，并具有较小的额外计算成本开销。提出的水平和垂直注意力是高度模块化的，可以将其插入各种变压器模型中，以进一步提高性能。我们的代码在补充材料中可用。

已被证明在改善神经电机翻译（NMT）系统方面有效的深度编码器，但是当编码器层数超过18时，它达到了翻译质量的上限。更糟糕的是，更深的网络消耗了很多内存，使其无法实现有效地训练。在本文中，我们呈现了共生网络，其包括完整的网络作为共生主网络（M-Net）和另一个具有相同结构的共享子网，但层数较少为共生子网（S-Net）。我们在变压器深度（M-N）架构上采用共生网络，并在NMT中定义M-Net和S-Net之间的特定正则化损耗$ \ mathcal {l} _ {\ tau} $。我们对共生网络进行联合培训，并旨在提高M净性能。我们拟议的培训策略在CMT'14 en-> De，De-> EN和EN-> FR任务的经典培训下将变压器深（12-6）改善了0.61,0.49和0.69 BLEU。此外，我们的变压器深（12-6）甚至优于经典变压器深度（18-6）。

多尺度特征层次结构已在计算机视觉区域的成功中得到了见证。这进一步激发了研究人员设计自然语言处理的多尺度变压器，主要是基于自我发项机制。例如，限制跨头部的接收场或通过卷积提取局部细粒度特征。但是，大多数现有作品都直接建模了本地功能，但忽略了单词边界信息。这导致了缺乏解释性的多余和模棱两可的注意力分布。在这项工作中，我们在不同的语言单元中定义了这些量表，包括子字，单词和短语。我们通过基于单词边界信息和短语级别的先验知识之间建立量表之间的关系来构建多尺度变压器模型。提出的\ textbf {u} niversal \ textbf {m} ulti \ textbf {s} cale \ textbf {t} ransformer，即在两个序列生成任务上评估。值得注意的是，它在几个测试组上的强大基线上产生了一致的性能，而无需牺牲效率。

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.

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset. * Equal contribution, alphabetically by last name. † Work performed while at Google Brain.

基于全注意力的变压器体系结构的强大建模能力通常会导致过度拟合，并且 - 对于自然语言处理任务，导致自动回归变压器解码器中隐式学习的内部语言模型，使外部语言模型的集成变得复杂。在本文中，我们探索了放松的注意力，对注意力的重量进行了简单易于实现的平滑平滑，从编码器。其次，我们表明它自然支持外部语言模型的整合，因为它通过放松解码器中的交叉注意来抑制隐式学习的内部语言模型。我们证明了在几项任务中放松注意力的好处，并与最近的基准方法相结合，并明显改善。具体而言，我们超过了最大的最大公共唇部阅读LRS3基准的26.90％单词错误率的先前最新性能，单词错误率为26.31％，并且我们达到了最佳表现的BLEU分数37.67在IWSLT14（de $ \ rightarrow $ en）的机器翻译任务没有外部语言模型，几乎没有其他模型参数。代码和模型将公开可用。

Transformer layers, which use an alternating pattern of multi-head attention and multi-layer perceptron (MLP) layers, provide an effective tool for a variety of machine learning problems. As the transformer layers use residual connections to avoid the problem of vanishing gradients, they can be viewed as the numerical integration of a differential equation. In this extended abstract, we build upon this connection and propose a modification of the internal architecture of a transformer layer. The proposed model places the multi-head attention sublayer and the MLP sublayer parallel to each other. Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks. Moreover, for the image classification task, we show that using neural ODE solvers with a sophisticated integration scheme further improves performance.

尽管变形金刚在段落的生成中取得了重大成功，但它们将句子视为令牌的线性序列，并且经常忽略其层次结构信息。先前的工作表明，输入令牌分解粒度〜（例如，单词，短语或句子）的水平已产生实质性改进，这表明可以通过更细粒度的粒度建模来增强变形金刚。在这项工作中，我们提出了粒度生成（C-DNPG）的粒度连续分解。为了有效地将粒度纳入编码句子中，C-DNPG引入了一种粒度感知的注意力（GA-注意）机制，该机制扩展了多头自我注意力，以：1）自动渗透句子的粒度头，该机制自动渗透了句子的等级结构通过神经估计每个输入令牌的粒度水平； 2）两个新的注意力面膜，即粒度共振和粒度范围，以有效地将粒度编码为注意力。在两个基准测试的实验（包括Quora问题对和Twitter URL）上表明，C-DNPG的表现优于基线模型，而在许多指标方面，C-DNPG的基线模型优于基线模型。定性分析表明，C-DNPG确实具有有效性捕获细粒度的粒度水平。

在完全共享所有语言参数的多语言神经机器翻译模型中，通常使用人工语言令牌来指导转换为所需的目标语言。但是，最近的研究表明，预备语言代币有时无法将多语言神经机器翻译模型导航到正确的翻译方向，尤其是在零弹性翻译上。为了减轻此问题，我们提出了两种方法：语言嵌入实施例和语言意识的多头关注，以学习信息丰富的语言表示，以将翻译转换为正确的方向。前者体现了沿着从源到目标的信息流中的不同关键切换点的语言，旨在放大翻译方向引导信号。后者利用矩阵而不是向量来表示连续空间中的语言。矩阵分为多个头，以学习多个子空间中的语言表示。在两个数据集上进行大规模多语言神经机器翻译的实验结果表明，语言意识到的多头注意力受益于监督和零弹性翻译，并大大减轻了脱靶翻译问题。进一步的语言类型学预测实验表明，通过我们的方法学到的基于基质的语言表示能够捕获丰富的语言类型学特征。