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arXiv:1706.03762v7 [cs.CL] 02 Aug 2023
arXiv:1706.03762v7 [cs.CL] 02 Aug 2023 (arXiv:1706.03762v7 [计算机语言学] 2023 年 8 月 2 日)

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Attention Is All You Need
注意力就是你所需要的一切 (Attention Is All You Need)

\ANDAshish Vaswani
Google Brain
avaswani@google.com
&Noam Shazeer11footnotemark: 1
Google Brain
noam@google.com
&Niki Parmar11footnotemark: 1
Google Research
nikip@google.com
&Jakob Uszkoreit11footnotemark: 1
Google Research
usz@google.com
&Llion Jones11footnotemark: 1
Google Research
llion@google.com
&Aidan N. Gomez11footnotemark: 1   
University of Toronto
aidan@cs.toronto.edu &Łukasz Kaiser11footnotemark: 1
Google Brain
lukaszkaiser@google.com
&Illia Polosukhin11footnotemark: 1  
illia.polosukhin@gmail.com
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.
Abstract 摘要

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 English-to-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 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
主导的序列转换模型基于复杂的循环或卷积神经网络,这些网络包括编码器和解码器。性能最佳的模型还通过注意力机制连接编码器和解码器。我们提出了一种新的简单网络架构,Transformer,完全基于注意力机制,彻底摒弃了循环和卷积。在两个机器翻译任务上的实验表明,这些模型在质量上更优,同时更易于并行化,并且训练所需时间显著减少。我们的模型在 WMT 2014 英德翻译任务上达到了 28.4 BLEU(BLEU),比现有最佳结果(包括集成模型)提高了超过 2 BLEU。在 WMT 2014 英法翻译任务上,我们的模型在 8 个 GPU 上训练 3.5 天后,建立了新的单模型最先进的 BLEU 得分 41.8,这只是文献中最佳模型训练成本的一小部分。我们展示了 Transformer 在其他任务上的良好泛化能力,通过成功应用于英语成分句法分析,无论是大规模还是有限的训练数据。 (WMT 2014: Workshop on Machine Translation 2014, 机器翻译研讨会 2014; BLEU: Bilingual Evaluation Understudy, 双语评估指标; GPU: Graphics Processing Unit, 图形处理单元)

1 Introduction 1 引言

Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15].
循环神经网络(Recurrent neural networks),特别是长短期记忆网络(long short-term memory [13])和门控循环神经网络(gated recurrent [7]),已被确立为序列建模和转换问题(如语言建模和机器翻译 [35, 2, 5])中的最先进方法(state of the art approaches)。此后,许多努力持续推动循环语言模型和编码器-解码器架构(encoder-decoder architectures [38, 24, 15])的界限。

Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states hth_{t}italic_h start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT, as a function of the previous hidden state ht1h_{t-1}italic_h start_POSTSUBSCRIPT italic_t - 1 end_POSTSUBSCRIPT and the input for position ttitalic_t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32], while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
循环模型通常会根据输入和输出序列的符号位置来分解计算。将这些位置与计算时间的步骤对齐,它们会生成一系列隐藏状态 htsubscripth_{t}italic_h start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT ,作为前一个隐藏状态 ht1subscript1h_{t-1}italic_h start_POSTSUBSCRIPT italic_t - 1 end_POSTSUBSCRIPT 和位置 ttitalic_t 的输入的函数。这种本质上的顺序性质排除了在训练样本内的并行化,这在序列长度较长时变得至关重要,因为内存限制限制了跨样本的批处理。最近的工作通过分解技巧 [21] 和条件计算 [32] 在计算效率上取得了显著的改进,同时在后者的情况下也提高了模型性能。然而,顺序计算的基本约束仍然存在。 (循环模型 Recurrent models: 一种处理序列数据的神经网络模型) (符号位置 symbol positions: 在序列中的每个元素的位置) (隐藏状态 hidden states: 神经网络中不直接输出但用于存储信息的状态) (分解计算 factor computation: 将复杂计算分解为更简单的步骤) (并行化 parallelization: 同时进行多个计算以提高效率) (内存限制 memory constraints: 计算机内存的限制) (批处理 batching: 将多个样本一起处理以提高效率) (分解技巧 factorization tricks: 通过将问题分解为更小的部分来简化计算) (条件计算 conditional computation: 根据输入的不同条件来决定计算路径)

Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms are used in conjunction with a recurrent network.
注意机制已经成为各种任务中引人注目的序列建模和转换模型不可或缺的一部分,使得建模依赖关系时无需考虑输入或输出序列中距离的远近 [2, 19]。然而,除了少数情况 [27] 之外,这些注意机制通常与循环网络结合使用。 (注意机制 Attention mechanisms) (序列建模 sequence modeling) (转换模型 transduction models) (依赖关系 dependencies) (循环网络 recurrent network)

In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
在本研究中,我们提出了 Transformer,一种模型架构,它摒弃了递归机制,完全依赖于注意力机制来捕捉输入和输出之间的全局依赖关系。Transformer 允许显著更多的并行化处理,并且在八个 P100 GPU 上训练仅需十二小时即可达到翻译质量的全新最先进水平(state of the art)。(P100 GPU: 英伟达的一种图形处理单元)

2 Background 2 背景

The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16], ByteNet [18] and ConvS2S [9], all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12]. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.
减少顺序计算的目标也是扩展神经 GPU [ 16]、ByteNet [ 18] 和 ConvS2S [ 9] 的基础,这些模型都使用卷积神经网络作为基本构建模块,并行计算所有输入和输出位置的隐藏表示。在这些模型中,关联两个任意输入或输出位置的信号所需的操作数量随着位置之间的距离增长,对于 ConvS2S 是线性增长,对于 ByteNet 是指数增长。这使得学习远距离位置之间的依赖关系变得更加困难 [ 12]。在 Transformer 中,这被简化为一个常数数量的操作,尽管由于平均注意力加权位置而降低了有效分辨率,我们通过第 3.2 节描述的多头注意力(Multi-Head Attention)来对抗这一效应。

Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22].
自注意力(Self-attention),有时也称为内部注意力(intra-attention),是一种注意力机制,它通过关联单个序列的不同位置来计算该序列的表示。自注意力已成功应用于多种任务,包括阅读理解、摘要生成、文本蕴含和学习任务无关的句子表示 [4, 27, 28, 22]。

End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34].
端到端记忆网络基于循环注意力机制而非序列对齐的循环,并且已被证明在简单语言问答和语言建模任务上表现良好 [34]。

To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9].
据我们所知,Transformer 是第一个完全依赖自注意力机制来计算其输入和输出的表示,而不使用序列对齐的 RNN 或卷积的转换模型。在接下来的章节中,我们将描述 Transformer,解释自注意力的动机,并讨论其相对于 [17, 18] 和 [9] 等模型的优势。

3 Model Architecture 3 模型架构 (3Model Architecture)

Refer to caption
Figure 1: The Transformer - model architecture.
图 1:Transformer - 模型架构。

Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35]. Here, the encoder maps an input sequence of symbol representations (x1,,xn)(x_{1},...,x_{n})( italic_x start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_x start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) to a sequence of continuous representations 𝐳=(z1,,zn)\mathbf{z}=(z_{1},...,z_{n})bold_z = ( italic_z start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_z start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ). Given 𝐳\mathbf{z}bold_z, the decoder then generates an output sequence (y1,,ym)(y_{1},...,y_{m})( italic_y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_y start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT ) of symbols one element at a time. At each step the model is auto-regressive [10], consuming the previously generated symbols as additional input when generating the next.
大多数竞争力的神经序列转换模型都采用编码器-解码器结构 [5, 2, 35]。在这里,编码器将输入的符号表示序列 (x1,,xn)subscript1subscript(x_{1},...,x_{n})( italic_x start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_x start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) 映射到一个连续表示序列 𝐳=(z1,,zn)subscript1subscript\mathbf{z}=(z_{1},...,z_{n})bold_z = ( italic_z start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_z start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) 。给定 𝐳\mathbf{z}bold_z ,解码器随后生成一个输出符号序列 (y1,,ym)subscript1subscript(y_{1},...,y_{m})( italic_y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_y start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT ) ,每次生成一个元素。在每个步骤中,模型是自回归的 [10],在生成下一个符号时会将之前生成的符号作为额外的输入。

The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.
Transformer 遵循这种整体架构,使用堆叠的自注意力机制和逐点、全连接层,分别用于编码器和解码器,如图 1 的左半部分和右半部分所示。

3.1 Encoder and Decoder Stacks
3.1 编码器和解码器堆栈 (Encoder and Decoder Stacks)

Encoder: 编码器:

The encoder is composed of a stack of N=6N=6italic_N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1]. That is, the output of each sub-layer is LayerNorm(x+Sublayer(x))\mathrm{LayerNorm}(x+\mathrm{Sublayer}(x))roman_LayerNorm ( italic_x + roman_Sublayer ( italic_x ) ), where Sublayer(x)\mathrm{Sublayer}(x)roman_Sublayer ( italic_x ) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension dmodel=512d_{\text{model}}=512italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT = 512.
编码器由 N=66N=6italic_N = 6 个相同的层堆叠而成。每个层有两个子层。第一个是多头自注意力机制,第二个是一个简单的、位置感知的全连接前馈网络。我们在两个子层周围都采用了残差连接 [ 11 ],随后是层归一化 [ 1 ]。也就是说,每个子层的输出是 LayerNorm(x+Sublayer(x))\mathrm{LayerNorm}(x+\mathrm{Sublayer}(x))roman_LayerNorm ( italic_x + roman_Sublayer ( italic_x ) ) ,其中 Sublayer(x)\mathrm{Sublayer}(x)roman_Sublayer ( italic_x ) 是子层本身实现的功能。为了便于这些残差连接,模型中的所有子层以及嵌入层都产生维度为 dmodel=512subscript512d_{\text{model}}=512italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT = 512 的输出。

Decoder: 解码器:

The decoder is also composed of a stack of N=6N=6italic_N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position iiitalic_i can depend only on the known outputs at positions less than iiitalic_i.
解码器同样由一堆 N=66N=6italic_N = 6 个相同的层组成。除了每个编码器层中的两个子层外,解码器还插入了一个第三子层,该子层对编码器堆栈的输出执行多头注意力机制。类似于编码器,我们在每个子层周围使用残差连接,然后进行层归一化。我们还修改了解码器堆栈中的自注意力子层,以防止位置关注到后续位置。这种掩码,结合输出嵌入偏移一个位置的事实,确保了位置 iiitalic_i 的预测只能依赖于小于 iiitalic_i 位置的已知输出。

3.2 Attention 3.2 注意力机制(Attention)

An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
一个注意力函数可以描述为将一个查询和一组键值对映射到一个输出,其中查询、键、值和输出都是向量。输出被计算为值的加权和,其中每个值的权重是通过查询与相应键的兼容性函数计算得出的。

3.2.1 Scaled Dot-Product Attention
3.2.1 缩放点积注意力机制 (Scaled Dot-Product Attention)

We call our particular attention "Scaled Dot-Product Attention" (Figure 2). The input consists of queries and keys of dimension dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT, and values of dimension dvd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT. We compute the dot products of the query with all keys, divide each by dk\sqrt{d_{k}}square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG, and apply a softmax function to obtain the weights on the values.
我们将这种特别的注意力机制称为“缩放点积注意力”(Scaled Dot-Product Attention)(图 2)。输入包括维度为 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT 的查询和键,以及维度为 dvsubscriptd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT 的值。我们计算查询与所有键的点积,将每个点积除以 dksubscript\sqrt{d_{k}}square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG ,然后应用 softmax 函数来获得值的权重。

In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix QQitalic_Q. The keys and values are also packed together into matrices KKitalic_K and VVitalic_V. We compute the matrix of outputs as:
在实践中,我们同时计算一组查询的注意力函数,将它们打包成一个矩阵 QQitalic_Q 。键和值也被打包成矩阵 KKitalic_KVVitalic_V 。我们计算输出矩阵如下:

Attention(Q,K,V)=softmax(QKTdk)V\mathrm{Attention}(Q,K,V)=\mathrm{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})Vroman_Attention ( italic_Q , italic_K , italic_V ) = roman_softmax ( divide start_ARG italic_Q italic_K start_POSTSUPERSCRIPT italic_T end_POSTSUPERSCRIPT end_ARG start_ARG square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG end_ARG ) italic_V (1)

The two most commonly used attention functions are additive attention [2], and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1dk\frac{1}{\sqrt{d_{k}}}divide start_ARG 1 end_ARG start_ARG square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG end_ARG. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.
最常用的两种注意力函数是加性注意力 [2] 和点积(乘性)注意力。点积注意力与我们的算法相同,只是有一个缩放因子 1dk1subscript\frac{1}{\sqrt{d_{k}}}divide start_ARG 1 end_ARG start_ARG square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG end_ARG 。加性注意力使用带有一个隐藏层的前馈网络来计算兼容性函数。虽然这两种方法在理论复杂度上相似,但在实践中,点积注意力要快得多且更节省空间,因为它可以使用高度优化的矩阵乘法代码来实现。

While for small values of dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT [3]. We suspect that for large values of dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients 111To illustrate why the dot products get large, assume that the components of qqitalic_q and kkitalic_k are independent random variables with mean 0 and variance 111. Then their dot product, qk=i=1dkqikiq\cdot k=\sum_{i=1}^{d_{k}}q_{i}k_{i}italic_q ⋅ italic_k = ∑ start_POSTSUBSCRIPT italic_i = 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUPERSCRIPT italic_q start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT italic_k start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT, has mean 0 and variance dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT.. To counteract this effect, we scale the dot products by 1dk\frac{1}{\sqrt{d_{k}}}divide start_ARG 1 end_ARG start_ARG square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG end_ARG.
对于 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT 的较小值,两个机制表现相似,但对于 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT 的较大值,加性注意力机制在未进行缩放的情况下表现优于点积注意力机制 [3]。我们怀疑,对于 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT 的较大值,点积的幅度会变得很大,将 softmax 函数推向梯度极小的区域 1。为了对抗这种效应,我们将点积缩放 1dk1subscript\frac{1}{\sqrt{d_{k}}}divide start_ARG 1 end_ARG start_ARG square-root start_ARG italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_ARG end_ARG (scaling factor)。

3.2.2 Multi-Head Attention
3.2.2 多头注意力机制 (Multi-Head Attention)

Scaled Dot-Product Attention
缩放点积注意力机制 (Scaled Dot-Product Attention)

Refer to caption

Multi-Head Attention 多头注意力机制 (Multi-Head Attention)

Refer to caption
Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.
图 2:(左) 缩放点积注意力机制(Scaled Dot-Product Attention)。(右) 多头注意力(Multi-Head Attention)由多个并行运行的注意力层组成。

Instead of performing a single attention function with dmodeld_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values hhitalic_h times with different, learned linear projections to dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT, dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT and dvd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding dvd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2.
我们发现,与其使用 dmodelsubscriptd_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT 维的键、值和查询执行单一的注意力函数,不如将查询、键和值分别进行 hhitalic_h 次线性投影,使用不同的、学习到的线性投影到 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPTdksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPTdvsubscriptd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT 维度上更有益。然后,我们并行地对这些投影后的查询、键和值执行注意力函数,得到 dvsubscriptd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT 维的输出值。这些输出值被拼接起来并再次投影,得到最终的值,如图 2 所示。

Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
多头注意力机制允许模型同时关注来自不同表示子空间和不同位置的信息。使用单个注意力头时,平均化会抑制这种效果。

MultiHead(Q,K,V)\displaystyle\mathrm{MultiHead}(Q,K,V)roman_MultiHead ( italic_Q , italic_K , italic_V ) =Concat(head1,,headh)WO\displaystyle=\mathrm{Concat}(\mathrm{head_{1}},...,\mathrm{head_{h}})W^{O}= roman_Concat ( roman_head start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , roman_head start_POSTSUBSCRIPT roman_h end_POSTSUBSCRIPT ) italic_W start_POSTSUPERSCRIPT italic_O end_POSTSUPERSCRIPT
whereheadi\displaystyle\text{where}~{}\mathrm{head_{i}}where roman_head start_POSTSUBSCRIPT roman_i end_POSTSUBSCRIPT =Attention(QWiQ,KWiK,VWiV)\displaystyle=\mathrm{Attention}(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i})= roman_Attention ( italic_Q italic_W start_POSTSUPERSCRIPT italic_Q end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_K italic_W start_POSTSUPERSCRIPT italic_K end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_V italic_W start_POSTSUPERSCRIPT italic_V end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT )

Where the projections are parameter matrices WiQdmodel×dkW^{Q}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}italic_W start_POSTSUPERSCRIPT italic_Q end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUPERSCRIPT, WiKdmodel×dkW^{K}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}italic_W start_POSTSUPERSCRIPT italic_K end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUPERSCRIPT, WiVdmodel×dvW^{V}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{v}}italic_W start_POSTSUPERSCRIPT italic_V end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT end_POSTSUPERSCRIPT and WOhdv×dmodelW^{O}\in\mathbb{R}^{hd_{v}\times d_{\text{model}}}italic_W start_POSTSUPERSCRIPT italic_O end_POSTSUPERSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_h italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_POSTSUPERSCRIPT.
其中投影是参数矩阵 WiQdmodel×dksubscriptsuperscriptsuperscriptsubscriptsubscriptW^{Q}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}italic_W start_POSTSUPERSCRIPT italic_Q end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUPERSCRIPTWiKdmodel×dksubscriptsuperscriptsuperscriptsubscriptsubscriptW^{K}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}italic_W start_POSTSUPERSCRIPT italic_K end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT end_POSTSUPERSCRIPTWiVdmodel×dvsubscriptsuperscriptsuperscriptsubscriptsubscriptW^{V}_{i}\in\mathbb{R}^{d_{\text{model}}\times d_{v}}italic_W start_POSTSUPERSCRIPT italic_V end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT end_POSTSUPERSCRIPTWOhdv×dmodelsuperscriptsuperscriptsubscriptsubscriptW^{O}\in\mathbb{R}^{hd_{v}\times d_{\text{model}}}italic_W start_POSTSUPERSCRIPT italic_O end_POSTSUPERSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_h italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT × italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_POSTSUPERSCRIPT

In this work we employ h=8h=8italic_h = 8 parallel attention layers, or heads. For each of these we use dk=dv=dmodel/h=64d_{k}=d_{v}=d_{\text{model}}/h=64italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT = italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT = italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT / italic_h = 64. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
在本工作中,我们采用了 h=88h=8italic_h = 8 个并行注意力层,或称为头部。对于每一个这样的头部,我们使用 dk=dv=dmodel/h=64subscriptsubscriptsubscript64d_{k}=d_{v}=d_{\text{model}}/h=64italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT = italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT = italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT / italic_h = 64 。由于每个头部的维度降低,总计算成本与具有完整维度的单头注意力相似。

3.2.3 Applications of Attention in our Model
3.2.3 在我们模型中注意力机制的应用 (Attention)

The Transformer uses multi-head attention in three different ways:
Transformer 使用多头注意力机制以三种不同的方式:

  • In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9].


    • 在“编码器-解码器注意力”层中,查询来自前一个解码器层,记忆键和值来自编码器的输出。这允许解码器中的每个位置都能关注输入序列中的所有位置。这模仿了序列到序列模型中典型的编码器-解码器注意力机制,如 [38, 2, 9] (sequence-to-sequence models)。
  • The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.


    • 编码器包含自注意力层(self-attention layers)。在自注意力层中,所有的键(keys)、值(values)和查询(queries)都来自同一个地方,在本例中,是编码器前一层的输出。编码器中的每个位置都可以关注到前一层编码器中的所有位置。
  • Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\infty- ∞) all values in the input of the softmax which correspond to illegal connections. See Figure 2.


    • 同样地,解码器中的自注意力层允许解码器中的每个位置关注解码器中直到该位置的所有位置。我们需要在解码器中阻止向左的信息流动以保持自回归特性。我们在缩放点积注意力内部通过将 softmax 输入中对应于非法连接的所有值屏蔽(设为 -\infty- ∞ )来实现这一点。见图 2。

3.3 Position-wise Feed-Forward Networks
3.3 位置前馈网络 (Position-wise Feed-Forward Networks)

In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.
除了注意力子层之外,我们的编码器和解码器中的每一层都包含一个完全连接的前馈网络,该网络分别且相同地应用于每个位置。这包括两个线性变换,中间有一个 ReLU 激活函数。

FFN(x)=max(0,xW1+b1)W2+b2\mathrm{FFN}(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2}roman_FFN ( italic_x ) = roman_max ( 0 , italic_x italic_W start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT + italic_b start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ) italic_W start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT + italic_b start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT (2)

While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1. The dimensionality of input and output is dmodel=512d_{\text{model}}=512italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT = 512, and the inner-layer has dimensionality dff=2048d_{ff}=2048italic_d start_POSTSUBSCRIPT italic_f italic_f end_POSTSUBSCRIPT = 2048.
在不同位置上的线性变换是相同的,但它们在层与层之间使用不同的参数。另一种描述方式是两个核大小为 1 的卷积。输入和输出的维度是 dmodel=512subscript512d_{\text{model}}=512italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT = 512 ,内层维度为 dff=2048subscript2048d_{ff}=2048italic_d start_POSTSUBSCRIPT italic_f italic_f end_POSTSUBSCRIPT = 2048

3.4 Embeddings and Softmax
3.4 嵌入和 Softmax (Embeddings and Softmax)

Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension dmodeld_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT. We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30]. In the embedding layers, we multiply those weights by dmodel\sqrt{d_{\text{model}}}square-root start_ARG italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_ARG.
与其他序列转换模型类似,我们使用学习到的嵌入(embeddings)将输入标记和输出标记转换为维度为 dmodelsubscriptd_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT 的向量。我们还使用常规的学习到的线性变换和 softmax 函数将解码器输出转换为预测的下一个标记的概率。在我们的模型中,我们在两个嵌入层和预 softmax 线性变换之间共享相同的权重矩阵,类似于[30]。在嵌入层中,我们将这些权重乘以 dmodelsubscript\sqrt{d_{\text{model}}}square-root start_ARG italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_ARG

3.5 Positional Encoding 3.5 位置编码

Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension dmodeld_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9].
由于我们的模型不包含循环和卷积,为了让模型利用序列的顺序,我们必须注入一些关于序列中标记的相对或绝对位置的信息。为此,我们在编码器和解码器堆栈的底部将“位置编码”添加到输入嵌入中。位置编码的维度与嵌入相同 dmodelsubscriptd_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT ,以便两者可以相加。位置编码有多种选择,既有学习的,也有固定的 [ 9]。

In this work, we use sine and cosine functions of different frequencies:
在本工作中,我们使用了不同频率的正弦和余弦函数:

PE(pos,2i)=sin(pos/100002i/dmodel)\displaystyle PE_{(pos,2i)}=sin(pos/10000^{2i/d_{\text{model}}})italic_P italic_E start_POSTSUBSCRIPT ( italic_p italic_o italic_s , 2 italic_i ) end_POSTSUBSCRIPT = italic_s italic_i italic_n ( italic_p italic_o italic_s / 10000 start_POSTSUPERSCRIPT 2 italic_i / italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_POSTSUPERSCRIPT )
PE(pos,2i+1)=cos(pos/100002i/dmodel)\displaystyle PE_{(pos,2i+1)}=cos(pos/10000^{2i/d_{\text{model}}})italic_P italic_E start_POSTSUBSCRIPT ( italic_p italic_o italic_s , 2 italic_i + 1 ) end_POSTSUBSCRIPT = italic_c italic_o italic_s ( italic_p italic_o italic_s / 10000 start_POSTSUPERSCRIPT 2 italic_i / italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT end_POSTSUPERSCRIPT )

where pospositalic_p italic_o italic_s is the position and iiitalic_i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2π2\pi2 italic_π to 100002π10000\cdot 2\pi10000 ⋅ 2 italic_π. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset kkitalic_k, PEpos+kPE_{pos+k}italic_P italic_E start_POSTSUBSCRIPT italic_p italic_o italic_s + italic_k end_POSTSUBSCRIPT can be represented as a linear function of PEposPE_{pos}italic_P italic_E start_POSTSUBSCRIPT italic_p italic_o italic_s end_POSTSUBSCRIPT.
在这里, pospositalic_p italic_o italic_s 是位置, iiitalic_i 是维度。也就是说,位置编码的每个维度对应一个正弦波。波长从 2π22\pi2 italic_π100002π10000210000\cdot 2\pi10000 ⋅ 2 italic_π 形成一个几何级数。我们选择这个函数是因为我们假设它能让模型更容易地通过相对位置来学习关注,因为对于任何固定的偏移量 kkitalic_kPEpos+ksubscriptPE_{pos+k}italic_P italic_E start_POSTSUBSCRIPT italic_p italic_o italic_s + italic_k end_POSTSUBSCRIPT 可以表示为 PEpossubscriptPE_{pos}italic_P italic_E start_POSTSUBSCRIPT italic_p italic_o italic_s end_POSTSUBSCRIPT 的线性函数。

We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
我们还尝试使用学习到的位置嵌入 [9],发现两种版本产生的结果几乎相同(见表 3 第(E)行)。我们选择了正弦版本,因为它可能允许模型外推到训练期间遇到的序列长度之外的长度。

4 Why Self-Attention 4 为什么自注意力机制 (Self-Attention)

In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x1,,xn)(x_{1},...,x_{n})( italic_x start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_x start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) to another sequence of equal length (z1,,zn)(z_{1},...,z_{n})( italic_z start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_z start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ), with xi,zidx_{i},z_{i}\in\mathbb{R}^{d}italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_z start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT, such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.
在本节中,我们将自注意力层(self-attention layers)的各个方面与常用于将一个可变长度的符号表示序列 (x1,,xn)subscript1subscript(x_{1},...,x_{n})( italic_x start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_x start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) 映射到另一个等长序列 (z1,,zn)subscript1subscript(z_{1},...,z_{n})( italic_z start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , italic_z start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT ) 的循环层(recurrent layers)和卷积层(convolutional layers)进行比较,其中 xi,zidsubscriptsubscriptsuperscriptx_{i},z_{i}\in\mathbb{R}^{d}italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , italic_z start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∈ blackboard_R start_POSTSUPERSCRIPT italic_d end_POSTSUPERSCRIPT ,例如在典型的序列转导编码器或解码器中的隐藏层。激发我们使用自注意力的原因,我们考虑了三个期望(desiderata)。

One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.
一个是每层的总计算复杂度。另一个是可以并行化的计算量,用所需的最小顺序操作次数来衡量。

The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12]. Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.
第三是网络中长距离依赖之间的路径长度。学习长距离依赖是许多序列转换任务中的一个关键挑战。影响学习这种依赖能力的一个关键因素是前向和后向信号在网络中必须穿越的路径长度。这些路径在输入和输出序列的任何组合位置之间的长度越短,学习长距离依赖就越容易 [12]。因此,我们还比较了由不同层类型组成的网络中任意两个输入和输出位置之间的最长路径长度。 (EXPLANATION: - long-range dependencies (长距离依赖): 指的是在序列数据中,两个元素之间的距离较远,但它们之间仍然存在某种关系或依赖性。 - sequence transduction tasks (序列转换任务): 指将一种序列(如文本、语音)转换为另一种序列的任务,例如机器翻译、语音识别等。 - forward and backward signals (前向和后向信号): 在神经网络中,前向信号是指从输入到输出的信号传递过程,后向信号是指在训练过程中通过反向传播算法进行的误差传递。 - path length (路径长度): 在网络中,指的是从一个节点到另一个节点所需经过的边的数量或距离。)

Table 1: Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. nnitalic_n is the sequence length, dditalic_d is the representation dimension, kkitalic_k is the kernel size of convolutions and rritalic_r the size of the neighborhood in restricted self-attention.
表 1:不同层类型的最大路径长度、每层复杂度和最少连续操作次数。 nnitalic_n 是序列长度, dditalic_d 是表示维度, kkitalic_k 是卷积的核大小, rritalic_r 是受限自注意力中的邻域大小。
Layer Type 层类型 Complexity per Layer 每层的复杂度 Sequential 顺序 Maximum Path Length 最大路径长度 (Maximum Path Length)
Operations 操作 (Operations)
Self-Attention 自注意力机制(Self-Attention) O(n2d)O(n^{2}\cdot d)italic_O ( italic_n start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ⋅ italic_d ) O(1)O(1)italic_O ( 1 ) O(1)O(1)italic_O ( 1 )
Recurrent 循环的 O(nd2)O(n\cdot d^{2})italic_O ( italic_n ⋅ italic_d start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) O(n)O(n)italic_O ( italic_n ) O(n)O(n)italic_O ( italic_n )
Convolutional 卷积 (Convolutional) O(knd2)O(k\cdot n\cdot d^{2})italic_O ( italic_k ⋅ italic_n ⋅ italic_d start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) O(1)O(1)italic_O ( 1 ) O(logk(n))O(log_{k}(n))italic_O ( italic_l italic_o italic_g start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_n ) )
Self-Attention (restricted)
自注意力(受限)(Self-Attention (restricted))
O(rnd)O(r\cdot n\cdot d)italic_O ( italic_r ⋅ italic_n ⋅ italic_d ) O(1)O(1)italic_O ( 1 ) O(n/r)O(n/r)italic_O ( italic_n / italic_r )

As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n)O(n)italic_O ( italic_n ) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length nnitalic_n is smaller than the representation dimensionality dditalic_d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size rritalic_r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r)O(n/r)italic_O ( italic_n / italic_r ). We plan to investigate this approach further in future work.
如表 1 所示,自注意力层以恒定数量的顺序执行操作连接所有位置,而循环层则需要 O(n)O(n)italic_O ( italic_n ) 个顺序操作。在计算复杂度方面,当序列长度 nnitalic_n 小于表示维度 dditalic_d 时,自注意力层比循环层更快,这在机器翻译中最先进模型使用的句子表示中通常是这种情况,例如词片[ 38]和字节对[ 31]表示。为了提高涉及非常长序列的任务的计算性能,可以限制自注意力仅考虑输入序列中以相应输出位置为中心的 rritalic_r 大小的邻域。这将使最大路径长度增加到 O(n/r)O(n/r)italic_O ( italic_n / italic_r ) 。我们计划在未来的工作中进一步研究这种方法。

A single convolutional layer with kernel width k<nk<nitalic_k < italic_n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k)O(n/k)italic_O ( italic_n / italic_k ) convolutional layers in the case of contiguous kernels, or O(logk(n))O(log_{k}(n))italic_O ( italic_l italic_o italic_g start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_n ) ) in the case of dilated convolutions [18], increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of kkitalic_k. Separable convolutions [6], however, decrease the complexity considerably, to O(knd+nd2)O(k\cdot n\cdot d+n\cdot d^{2})italic_O ( italic_k ⋅ italic_n ⋅ italic_d + italic_n ⋅ italic_d start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ). Even with k=nk=nitalic_k = italic_n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.
一个卷积层,核宽度为 k<nk<nitalic_k < italic_n ,并不会连接所有输入和输出位置的对。要做到这一点,在连续核的情况下,需要堆叠 O(n/k)O(n/k)italic_O ( italic_n / italic_k ) 个卷积层,或者在扩张卷积的情况下,需要 O(logk(n))subscriptO(log_{k}(n))italic_O ( italic_l italic_o italic_g start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT ( italic_n ) ) 个卷积层[18],这增加了网络中任意两个位置之间最长路径的长度。卷积层通常比循环层更昂贵,成本高出 kkitalic_k 倍。然而,可分离卷积[6]大大降低了复杂性,降至 O(knd+nd2)superscript2O(k\cdot n\cdot d+n\cdot d^{2})italic_O ( italic_k ⋅ italic_n ⋅ italic_d + italic_n ⋅ italic_d start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) 。即便如此,可分离卷积的复杂度也等于自注意力层和逐点前馈层的组合,这是我们在模型中采用的方法。

As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.
作为副作用,自注意力机制可以产生更易于解释的模型。我们检查了模型中的注意力分布,并在附录中展示和讨论了示例。不仅单个注意头明显学会了执行不同的任务,许多注意头还表现出与句子的句法和语义结构相关的行为。

5 Training

This section describes the training regime for our models.
本节描述了我们模型的训练方案。

5.1 Training Data and Batching
5.1 训练数据和批处理 (Training Data and Batching)

We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3], which has a shared source-target vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38]. Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.
我们使用标准的 WMT 2014 英德数据集进行了训练,该数据集包含大约 450 万个句子对。句子使用字节对编码进行了编码[3],这有一个共享的源-目标词汇表,大约有 37000 个词元。对于英法语,我们使用了更大的 WMT 2014 英法数据集,包含 3600 万个句子,并将词元分割成一个 32000 个词片的词汇表[38]。句子对按近似序列长度分批处理。每个训练批次包含一组句子对,其中包含大约 25000 个源词元和 25000 个目标词元。 (WMT 2014 英德数据集: Workshop on Machine Translation 2014 English-German dataset) (字节对编码: Byte-Pair Encoding) (WMT 2014 英法数据集: Workshop on Machine Translation 2014 English-French dataset)

5.2 Hardware and Schedule 5.2 硬件和进度表 (Hardware and Schedule)

We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).
我们在配备 8 个 NVIDIA P100 GPU(英伟达 P100 图形处理单元)的单台机器上训练了我们的模型。对于使用论文中描述的超参数的基本模型,每个训练步骤大约需要 0.4 秒。我们训练了基本模型共计 100,000 步或 12 小时。对于我们的“大模型”(在表 3 的底行描述),每个步骤的时间为 1.0 秒。这些大模型被训练了 300,000 步(3.5 天)。

5.3 Optimizer 5.3 优化器(Optimizer)

We used the Adam optimizer [20] with β1=0.9\beta_{1}=0.9italic_β start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT = 0.9, β2=0.98\beta_{2}=0.98italic_β start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT = 0.98 and ϵ=109\epsilon=10^{-9}italic_ϵ = 10 start_POSTSUPERSCRIPT - 9 end_POSTSUPERSCRIPT. We varied the learning rate over the course of training, according to the formula:
我们使用了 Adam 优化器 [ 20] 与 β1=0.9subscript10.9\beta_{1}=0.9italic_β start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT = 0.9 , β2=0.98subscript20.98\beta_{2}=0.98italic_β start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT = 0.98ϵ=109superscript109\epsilon=10^{-9}italic_ϵ = 10 start_POSTSUPERSCRIPT - 9 end_POSTSUPERSCRIPT 。我们在训练过程中根据公式调整了学习率:

lrate=dmodel0.5min(step_num0.5,step_numwarmup_steps1.5)lrate=d_{\text{model}}^{-0.5}\cdot\min({step\_num}^{-0.5},{step\_num}\cdot{% warmup\_steps}^{-1.5})italic_l italic_r italic_a italic_t italic_e = italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT start_POSTSUPERSCRIPT - 0.5 end_POSTSUPERSCRIPT ⋅ roman_min ( italic_s italic_t italic_e italic_p _ italic_n italic_u italic_m start_POSTSUPERSCRIPT - 0.5 end_POSTSUPERSCRIPT , italic_s italic_t italic_e italic_p _ italic_n italic_u italic_m ⋅ italic_w italic_a italic_r italic_m italic_u italic_p _ italic_s italic_t italic_e italic_p italic_s start_POSTSUPERSCRIPT - 1.5 end_POSTSUPERSCRIPT ) (3)

This corresponds to increasing the learning rate linearly for the first warmup_stepswarmup\_stepsitalic_w italic_a italic_r italic_m italic_u italic_p _ italic_s italic_t italic_e italic_p italic_s training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps=4000warmup\_steps=4000italic_w italic_a italic_r italic_m italic_u italic_p _ italic_s italic_t italic_e italic_p italic_s = 4000.
这对应于在前 warmup_stepswarmup\_stepsitalic_w italic_a italic_r italic_m italic_u italic_p _ italic_s italic_t italic_e italic_p italic_s 个训练步骤中线性增加学习率,此后按步数的平方根倒数比例降低学习率。我们使用了 warmup_steps=40004000warmup\_steps=4000italic_w italic_a italic_r italic_m italic_u italic_p _ italic_s italic_t italic_e italic_p italic_s = 4000

5.4 Regularization 5.4 正则化 (Regularization)

We employ three types of regularization during training:
在训练过程中,我们采用了三种正则化方法:

Residual Dropout 残差丢弃 (Residual Dropout)

We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of Pdrop=0.1P_{drop}=0.1italic_P start_POSTSUBSCRIPT italic_d italic_r italic_o italic_p end_POSTSUBSCRIPT = 0.1.
我们在每个子层的输出上应用 dropout [33],在将其添加到子层输入并进行归一化之前。此外,我们在编码器和解码器堆栈中的嵌入和位置编码的总和上也应用 dropout。对于基础模型,我们使用 Pdrop=0.1subscript0.1P_{drop}=0.1italic_P start_POSTSUBSCRIPT italic_d italic_r italic_o italic_p end_POSTSUBSCRIPT = 0.1 的速率。

Label Smoothing 标签平滑 (Label Smoothing)

During training, we employed label smoothing of value ϵls=0.1\epsilon_{ls}=0.1italic_ϵ start_POSTSUBSCRIPT italic_l italic_s end_POSTSUBSCRIPT = 0.1 [36]. This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.
在训练过程中,我们采用了值为 ϵls=0.1subscript0.1\epsilon_{ls}=0.1italic_ϵ start_POSTSUBSCRIPT italic_l italic_s end_POSTSUBSCRIPT = 0.1 的标签平滑技术[36]。这会降低困惑度,因为模型学会了更加不确定,但可以提高准确性和 BLEU 分数。

6 Results

6.1 Machine Translation 6.1 机器翻译

Table 2: The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.
表 2:Transformer 在英语到德语和英语到法语的 newstest2014 测试中取得了比之前最先进模型更好的 BLEU 分数,且训练成本仅为其一小部分。
Model BLEU Training Cost (FLOPs) 训练成本(FLOPs)
EN-DE EN-FR EN-DE EN-FR
ByteNet [18] ByteNet [18] (字节网络 [18]) 23.75
Deep-Att + PosUnk [39]
Deep-Att + PosUnk [39] (Deep-Att + PosUnk 是指深度注意力机制加上位置未知标记的模型,[39] 可能指的是相关文献或数据集的编号)
39.2 1.010201.0\cdot 10^{20}1.0 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT
GNMT + RL [38]
GNMT + RL [38] (GNMT: Google Neural Machine Translation, RL: Reinforcement Learning)
24.6 39.92 2.310192.3\cdot 10^{19}2.3 ⋅ 10 start_POSTSUPERSCRIPT 19 end_POSTSUPERSCRIPT 1.410201.4\cdot 10^{20}1.4 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT
ConvS2S [9] ConvS2S [9] 25.16 40.46 9.610189.6\cdot 10^{18}9.6 ⋅ 10 start_POSTSUPERSCRIPT 18 end_POSTSUPERSCRIPT 1.510201.5\cdot 10^{20}1.5 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT
MoE [32] MoE [32] (MoE: 混合专家模型, 32: 指模型中的专家数量) 26.03 40.56 2.010192.0\cdot 10^{19}2.0 ⋅ 10 start_POSTSUPERSCRIPT 19 end_POSTSUPERSCRIPT 1.210201.2\cdot 10^{20}1.2 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT
Deep-Att + PosUnk Ensemble [39]
Deep-Att + PosUnk 集成 [39] (集成:Ensemble,指多种模型或方法的组合)
40.4 8.010208.0\cdot 10^{20}8.0 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT
GNMT + RL Ensemble [38]
GNMT + RL 集成 [38] (GNMT: Google Neural Machine Translation, RL: Reinforcement Learning, 集成: Ensemble)
26.30 41.16 1.810201.8\cdot 10^{20}1.8 ⋅ 10 start_POSTSUPERSCRIPT 20 end_POSTSUPERSCRIPT 1.110211.1\cdot 10^{21}1.1 ⋅ 10 start_POSTSUPERSCRIPT 21 end_POSTSUPERSCRIPT
ConvS2S Ensemble [9] ConvS2S 集成 [ 9] 26.36 41.29 7.710197.7\cdot 10^{19}7.7 ⋅ 10 start_POSTSUPERSCRIPT 19 end_POSTSUPERSCRIPT 1.210211.2\cdot 10^{21}1.2 ⋅ 10 start_POSTSUPERSCRIPT 21 end_POSTSUPERSCRIPT
Transformer (base model) Transformer(基础模型) 27.3 38.1 3.3𝟏𝟎𝟏𝟖3.3\cdot 10^{18}bold_3.3 bold_⋅ bold_10 start_POSTSUPERSCRIPT bold_18 end_POSTSUPERSCRIPT
Transformer (big) Transformer (big) (变压器(大型)) 28.4 41.8 2.310192.3\cdot 10^{19}2.3 ⋅ 10 start_POSTSUPERSCRIPT 19 end_POSTSUPERSCRIPT

On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.02.02.0 BLEU, establishing a new state-of-the-art BLEU score of 28.428.428.4. The configuration of this model is listed in the bottom line of Table 3. Training took 3.53.53.5 days on 888 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.
在 WMT 2014 英译德任务中,大型变换器模型(表 2 中的 Transformer (big))在 BLEU 分数上超过了之前报道的最佳模型(包括集成模型)超过 2.02.02.02.0 BLEU,创下了新的最先进 BLEU 分数 28.428.428.428.4 。该模型的配置在表 3 的底行列出。训练耗时 3.53.53.53.5 天,使用了 8888 个 P100 GPU(英伟达 P100 图形处理单元)。即使是我们的基础模型也超过了所有之前发布的模型和集成模型,且训练成本仅为竞争模型的一小部分。

On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.041.041.0, outperforming all of the previously published single models, at less than 1/41/41 / 4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate Pdrop=0.1P_{drop}=0.1italic_P start_POSTSUBSCRIPT italic_d italic_r italic_o italic_p end_POSTSUBSCRIPT = 0.1, instead of 0.30.30.3.
在 WMT 2014 英译法任务中,我们的大模型达到了 41.041.041.041.0 的 BLEU 分数,超过了所有先前发布的单一模型,并且训练成本不到之前最先进模型的 1/4141/41 / 4 。Transformer(大)模型在英译法训练中使用了 Pdrop=0.1subscript0.1P_{drop}=0.1italic_P start_POSTSUBSCRIPT italic_d italic_r italic_o italic_p end_POSTSUBSCRIPT = 0.1 的 dropout 率,而不是 0.30.30.30.3

For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 444 and length penalty α=0.6\alpha=0.6italic_α = 0.6 [38]. These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 505050, but terminate early when possible [38].
对于基础模型,我们使用了通过平均最后 5 个检查点获得的单一模型,这些检查点是以 10 分钟为间隔写入的。对于大型模型,我们平均了最后 20 个检查点。我们使用了束搜索,束大小为 4444 ,长度惩罚为 α=0.60.6\alpha=0.6italic_α = 0.6 [ 38]。这些超参数是在开发集上实验后选择的。我们将推理时的最大输出长度设置为输入长度 + 50505050 ,但在可能的情况下提前终止 [ 38]。

Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU 222We used values of 2.8, 3.7, 6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively..
表 2 总结了我们的结果,并将我们的翻译质量和训练成本与文献中的其他模型架构进行了比较。我们通过将训练时间、使用的 GPU 数量以及每个 GPU 的持续单精度浮点运算能力估计值相乘,来估算训练一个模型所使用的浮点运算次数(FLOPs)。(FLOPs: Floating Point Operations)

6.2 Model Variations 6.2 模型变体

Table 3: Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.
表 3:Transformer 架构的变体。未列出的值与基础模型相同。所有指标均基于英语到德语的翻译开发集,newstest2013。所列的困惑度是按我们的字节对编码计算的每个词片的困惑度,不应与每个词的困惑度进行比较。
NNitalic_N dmodeld_{\text{model}}italic_d start_POSTSUBSCRIPT model end_POSTSUBSCRIPT dffd_{\text{ff}}italic_d start_POSTSUBSCRIPT ff end_POSTSUBSCRIPT hhitalic_h dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT dvd_{v}italic_d start_POSTSUBSCRIPT italic_v end_POSTSUBSCRIPT PdropP_{drop}italic_P start_POSTSUBSCRIPT italic_d italic_r italic_o italic_p end_POSTSUBSCRIPT ϵls\epsilon_{ls}italic_ϵ start_POSTSUBSCRIPT italic_l italic_s end_POSTSUBSCRIPT train PPL BLEU params
steps (dev) (dev) ×106\times 10^{6}× 10 start_POSTSUPERSCRIPT 6 end_POSTSUPERSCRIPT
base 6 512 2048 8 64 64 0.1 0.1 100K 4.92 25.8 65
(A) 1 512 512 5.29 24.9
4 128 128 5.00 25.5
16 32 32 4.91 25.8
32 16 16 5.01 25.4
(B) 16 5.16 25.1 58
32 5.01 25.4 60
(C) 2 6.11 23.7 36
4 5.19 25.3 50
8 4.88 25.5 80
256 32 32 5.75 24.5 28
1024 128 128 4.66 26.0 168
1024 5.12 25.4 53
4096 4.75 26.2 90
(D) 0.0 5.77 24.6
0.2 4.95 25.5
0.0 4.67 25.3
0.2 5.47 25.7
(E) positional embedding instead of sinusoids
位置嵌入而不是正弦波 (positional embedding instead of sinusoids)
4.92 25.7
big 6 1024 4096 16 0.3 300K 4.33 26.4 213

To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the development set, newstest2013. We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3.
为了评估 Transformer 中不同组件的重要性,我们以不同的方式调整了基础模型,测量其在英语到德语翻译任务上的性能变化,测试集为 newstest2013。我们使用了前一节描述的束搜索(beam search),但没有使用检查点平均(checkpoint averaging)。我们在表 3 中展示了这些结果。

In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.
在表 3 的行(A)中,我们改变了注意力头的数量以及注意力键和值的维度,保持计算量不变,如第 3.2.2 节所述。虽然单头注意力比最佳设置差 0.9 BLEU,但过多的头也会导致质量下降。

In Table 3 rows (B), we observe that reducing the attention key size dkd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9], and observe nearly identical results to the base model.
在表 3 的行(B)中,我们观察到减少注意力键的大小 dksubscriptd_{k}italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT 会损害模型质量。这表明确定兼容性并不容易,可能需要比点积更复杂的兼容性函数。我们进一步观察到在行(C)和(D)中,符合预期的是,模型越大越好,而 dropout 在避免过拟合方面非常有帮助。在行(E)中,我们用学习到的位置嵌入[ 9 ]替换了我们的正弦位置编码,并观察到与基础模型几乎相同的结果。

6.3 English Constituency Parsing
6.3 英语选区解析 (English Constituency Parsing)

Table 4: The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)
表 4:Transformer 在英语成分句法分析中表现出良好的泛化能力(结果见 WSJ 第 23 节)(WSJ: 华尔街日报)
Parser 解析器 (Parser) Training 培训 WSJ 23 F1 WSJ 23 F1 (华尔街日报 2023 年第一季度)
Vinyals & Kaiser el al. (2014) [37]  WSJ only, discriminative
WSJ only, 区分性 (WSJ: 华尔街日报)
88.3
Petrov et al. (2006) [29]
Petrov 等人(2006)[29]
WSJ only, discriminative
WSJ only, 区分性 (WSJ: 华尔街日报)
90.4
Zhu et al. (2013) [40]
Zhu 等人 (2013) [40]
WSJ only, discriminative
WSJ only, 区分性 (WSJ: 华尔街日报)
90.4
Dyer et al. (2016) [8]  WSJ only, discriminative  91.7
Transformer (4 layers)  WSJ only, discriminative  91.3
Zhu et al. (2013) [40]  semi-supervised  91.3
Huang & Harper (2009) [14]
Huang & Harper (2009) [ 14] (黄与哈珀,2009 年,第 14 页)
semi-supervised 半监督 (semi-supervised) 91.3
McClosky et al. (2006) [26]
McClosky 等人(2006)[ 26] (McClosky 是人名)
semi-supervised 半监督 (semi-supervised) 92.1
Vinyals & Kaiser el al. (2014) [37]
Vinyals & Kaiser 等人 (2014) [37]
semi-supervised 半监督 (semi-supervised) 92.1
Transformer (4 layers) Transformer(4 层) semi-supervised 半监督 (semi-supervised) 92.7
Luong et al. (2015) [23]
Luong 等人 (2015) [23] (Luong et al. 指的是 Luong 和他的合作者们)
multi-task 多任务(multi-task) 93.0
Dyer et al. (2016) [8]  generative  93.3

To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37].
为了评估 Transformer 是否能够泛化到其他任务,我们在英语成分句法分析上进行了实验。这一任务提出了特定的挑战:输出受强结构约束,并且显著长于输入。此外,RNN 序列到序列模型在小数据环境下未能达到最先进的结果 [37]。(Transformer: 一种神经网络架构;English constituency parsing: 英语成分句法分析;RNN: 递归神经网络;state-of-the-art: 最先进的)

We trained a 4-layer transformer with dmodel=1024d_{model}=1024italic_d start_POSTSUBSCRIPT italic_m italic_o italic_d italic_e italic_l end_POSTSUBSCRIPT = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25], about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37]. We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.
我们训练了一个 4 层变换器(transformer)模型,使用 dmodel=1024subscript1024d_{model}=1024italic_d start_POSTSUBSCRIPT italic_m italic_o italic_d italic_e italic_l end_POSTSUBSCRIPT = 1024 在宾夕法尼亚树库(Penn Treebank)[25]的华尔街日报(Wall Street Journal, WSJ)部分,约 40K 个训练句子。我们还在半监督环境下进行了训练,使用了更大的高置信度和伯克利解析器(BerkleyParser)语料库,约 17M 个句子[37]。我们在仅 WSJ 设置中使用了 16K 个词汇表,在半监督设置中使用了 32K 个词汇表。

We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300300300. We used a beam size of 212121 and α=0.3\alpha=0.3italic_α = 0.3 for both WSJ only and the semi-supervised setting.
我们仅进行了少量实验来选择 dropout、注意力和残差(章节 5.4)、学习率和 beam size,这些实验是在第 22 节的开发集上进行的,所有其他参数保持与英德基础翻译模型相同。在推理过程中,我们将最大输出长度增加到输入长度 + 300300300300 。我们在 WSJ 单独和半监督设置中都使用了 21212121α=0.30.3\alpha=0.3italic_α = 0.3 的 beam size。

Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8].
我们的结果在表 4 中显示,尽管缺乏特定任务的调优,我们的模型表现得出奇地好,产生的结果优于所有先前报道的模型,除了循环神经网络语法模型(Recurrent Neural Network Grammar [8])之外。

In contrast to RNN sequence-to-sequence models [37], the Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.
与 RNN 序列到序列模型[37]相比,Transformer 即使只在 WSJ 训练集的 40K 句子上训练,也超过了 BerkeleyParser[29]的表现。 (BerkeleyParser: 伯克利解析器; WSJ: 华尔街日报)

7 Conclusion 7 结论

In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.
在本工作中,我们提出了 Transformer,这是第一个完全基于注意力机制的序列转换模型,它用多头自注意力机制取代了编码器-解码器架构中最常用的循环层。

For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.
在翻译任务中,Transformer 的训练速度比基于循环或卷积层的架构快得多。在 WMT 2014 英德翻译任务和 WMT 2014 英法翻译任务上,我们达到了新的最先进水平。在前者任务中,我们的最佳模型甚至超过了之前报道的所有集成模型。 (WMT 2014: Workshop on Machine Translation 2014)

We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.
我们对基于注意力机制的模型的未来感到兴奋,并计划将其应用于其他任务。我们计划将 Transformer 扩展到处理除文本之外的其他输入和输出模式的问题,并研究局部、受限的注意力机制,以高效处理大规模输入和输出,如图像、音频和视频。使生成过程更少依赖序列是我们的另一个研究目标。

The code we used to train and evaluate our models is available at https://github.com/tensorflow/tensor2tensor.
我们用来训练和评估模型的代码可以在以下链接找到:https://github.com/tensorflow/tensor2tensor。

Acknowledgements 致谢

We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.
我们感谢 Nal Kalchbrenner 和 Stephan Gouws(他们是人工智能领域的专家)提出的宝贵意见、修正和灵感。

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Attention Visualizations 注意力可视化 (Attention Visualizations)

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Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb ‘making’, completing the phrase ‘making…more difficult’. Attentions here shown only for the word ‘making’. Different colors represent different heads. Best viewed in color.
图 3:编码器自注意力机制在第 5 层中的长距离依赖性示例。许多注意力头关注到动词‘making’(制作)的远距离依赖,完成短语‘making…more difficult’(使……更加困难)。此处仅显示‘making’一词的注意力。不同颜色代表不同的头部。彩色效果最佳。
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Figure 4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word ‘its’ for attention heads 5 and 6. Note that the attentions are very sharp for this word.
图 4:两个注意力头,同样位于 6 层中的第 5 层,显然参与了指代消解。顶部:注意力头 5 的完整注意力。底部:仅来自单词‘its’的隔离注意力,针对注意力头 5 和 6。注意这个词的注意力非常集中。
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Figure 5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.
图 5:许多注意力头表现出与句子结构相关的行为。我们在上方给出了两个这样的例子,来自第 5 层(共 6 层)的编码器自注意力中的两个不同头。这些头显然学会了执行不同的任务。