The best answers are voted up and rise to the top, Not the answer you're looking for? A Medium publication sharing concepts, ideas and codes. Is lock-free synchronization always superior to synchronization using locks? What problems does each other solve that the other can't? vegan) just to try it, does this inconvenience the caterers and staff? Does Cast a Spell make you a spellcaster? $$, $$ labeled by the index Weight matrices for query, key, vector respectively. Also, the first paper mentions additive attention is more computationally expensive, but I am having trouble understanding how. I just wanted to add a picture for a better understanding to the @shamane-siriwardhana, the main difference is in the output of the decoder network. 2-layer decoder. Connect and share knowledge within a single location that is structured and easy to search. This mechanism refers to Dzmitry Bahdanaus work titled Neural Machine Translation by Jointly Learning to Align and Translate. The scaled dot-product attention computes the attention scores based on the following mathematical formulation: Source publication Incorporating Inner-word and Out-word Features for Mongolian . Finally, in order to calculate our context vector we pass the scores through a softmax, multiply with a corresponding vector and sum them up. w Find centralized, trusted content and collaborate around the technologies you use most. As a result, conventional self-attention is tightly coupled by nature, which prevents the extraction of intra-frame and inter-frame action features and thereby degrades the overall performance of . Also, I saw that new posts are share every month, this one for example is really well made, hope you'll find it useful: @Avatrin The weight matrices Eduardo is talking about here are not the raw dot product softmax wij that Bloem is writing about at the beginning of the article. @Zimeo the first one dot, measures the similarity directly using dot product. What is the difference between Attention Gate and CNN filters? $\mathbf{V}$ refers to the values vectors matrix, $v_i$ being a single value vector associated with a single input word. The dot products yield values anywhere between negative and positive infinity, so a softmax is applied to map the values to [0,1] and to ensure that they sum to 1 over the whole sequence. we don't really know why the BatchNorm works, We've added a "Necessary cookies only" option to the cookie consent popup. Whereas key, is the hidden state of the encoder, and the corresponding value is normalized weight, representing how much attention a key gets. The best answers are voted up and rise to the top, Not the answer you're looking for? 500-long context vector = H * w. c is a linear combination of h vectors weighted by w. Upper case variables represent the entire sentence, and not just the current word. torch.matmul(input, other, *, out=None) Tensor. But in the Bahdanau at time t we consider about t-1 hidden state of the decoder. If you are a bit confused a I will provide a very simple visualization of dot scoring function. S, decoder hidden state; T, target word embedding. Thus, at each timestep, we feed our embedded vectors as well as a hidden state derived from the previous timestep. It mentions content-based attention where the alignment scoring function for the $j$th encoder hidden state with respect to the $i$th context vector is the cosine distance: $$ Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. The output is a 100-long vector w. 500100. This image shows basically the result of the attention computation (at a specific layer that they don't mention). What are the consequences? Thus, we expect this scoring function to give probabilities of how important each hidden state is for the current timestep. Read More: Effective Approaches to Attention-based Neural Machine Translation. Do EMC test houses typically accept copper foil in EUT? Making statements based on opinion; back them up with references or personal experience. Is it a shift scalar, weight matrix or something else? The weights are obtained by taking the softmax function of the dot product Assume you have a sequential decoder, but in addition to the previous cells output and hidden state, you also feed in a context vector c. Where c is a weighted sum of the encoder hidden states. Am I correct? The computations involved can be summarised as follows. t Papers With Code is a free resource with all data licensed under, methods/Screen_Shot_2020-05-25_at_12.32.09_PM.png, Effective Approaches to Attention-based Neural Machine Translation. The probability assigned to a given word in the pointer vocabulary distribution is the sum of the probabilities given to all token positions where the given word appears. What's more, is that in Attention is All you Need they introduce the scaled dot product where they divide by a constant factor (square root of size of encoder hidden vector) to avoid vanishing gradients in the softmax. The so obtained self-attention scores are tiny for words which are irrelevant for the chosen word. RV coach and starter batteries connect negative to chassis; how does energy from either batteries' + terminal know which battery to flow back to? Any insight on this would be highly appreciated. I think my main takeaways from your answer are a) cosine distance doesn't take scale into account, b) they divide by $sqrt(d_k)$ but it could have been something else and might have worked and we don't really know why, By the way, re layer norm vs batch norm I also have. It also explains why it makes sense to talk about multi-head attention. Finally, our context vector looks as above. The Bandanau variant uses a concatenative (or additive) instead of the dot product/multiplicative forms. Suppose our decoders current hidden state and encoders hidden states look as follows: Now we can calculate scores with the function above. {\displaystyle k_{i}} Dictionary size of input & output languages respectively. What is the difference between additive and multiplicative attention? So, the coloured boxes represent our vectors, where each colour represents a certain value. To learn more, see our tips on writing great answers. Additive and multiplicative attention are similar in complexity, although multiplicative attention is faster and more space-efficient in practice as it can be implemented more efficiently using matrix multiplication. Here f is an alignment model which scores how well the inputs around position j and the output at position i match, and s is the hidden state from the previous timestep. Dot-Product Attention is an attention mechanism where the alignment score function is calculated as: f a t t ( h i, s j) = h i T s j It is equivalent to multiplicative attention (without a trainable weight matrix, assuming this is instead an identity matrix). The present study tested the intrinsic ERP features of the effects of acute psychological stress on speed perception. The first option, which is dot, is basically a dot product of hidden states of the encoder (h_s) and the hidden state of the decoder (h_t). Dot product of vector with camera's local positive x-axis? Chapter 5 explains motor control from a closed-loop perspective, in which it examines the sensory contributions to movement control, with particular emphasis on new research regarding the . The behavior depends on the dimensionality of the tensors as follows: If both tensors are 1-dimensional, the dot product (scalar) is returned. (2) LayerNorm and (3) your question about normalization in the attention The above work (Jupiter Notebook) can be easily found on my GitHub. How does a fan in a turbofan engine suck air in? How to combine multiple named patterns into one Cases? Dot Product Attention (Multiplicative) We will cover this more in Transformer tutorial. It is widely used in various sub-fields, such as natural language processing or computer vision. Each The main difference is how to score similarities between the current decoder input and encoder outputs. i PTIJ Should we be afraid of Artificial Intelligence? How can I recognize one? It means a Dot-Product is scaled. I hope it will help you get the concept and understand other available options. is assigned a value vector {\displaystyle i} By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. vegan) just to try it, does this inconvenience the caterers and staff? $\mathbf{K}$ refers to the keys vectors matrix, $k_i$ being a single key vector associated with a single input word. By providing a direct path to the inputs, attention also helps to alleviate the vanishing gradient problem. QK1K2 KnattentionQ-K1Q-K2softmax, dot-product attention Q K V dot-product attentionVQQKQVTransformerdot-product attentiondkdot-product attention, dot-product attention Q K What is the intuition behind the dot product attention? Local attention is a combination of soft and hard attention, Luong gives us many other ways to calculate the attention weights..most involving a dot product..hence the name multiplcative. Scaled Dot Product Attention Self-Attention . The paper 'Pointer Sentinel Mixture Models'[2] uses self-attention for language modelling. Here $\textbf{h}$ refers to the hidden states for the encoder, and $\textbf{s}$ is the hidden states for the decoder. We need to score each word of the input sentence against this word. When we set W_a to the identity matrix both forms coincide. Why does the impeller of a torque converter sit behind the turbine? I went through the pytorch seq2seq tutorial. Luong attention used top hidden layer states in both of encoder and decoder. How to derive the state of a qubit after a partial measurement? Normalization - analogously to batch normalization it has trainable mean and We suspect that for large values of d k, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely . At first I thought that it settles your question: since the context vector)? Asking for help, clarification, or responding to other answers. Otherwise both attentions are soft attentions. Then explain one advantage and one disadvantage of additive attention compared to multiplicative attention. Is variance swap long volatility of volatility? {\textstyle \sum _{i}w_{i}=1} But, please, note that some words are actually related even if not similar at all, for example, 'Law' and 'The' are not similar, they are simply related to each other in these specific sentences (that's why I like to think of attention as a coreference resolution). If you are new to this area, lets imagine that the input sentence is tokenized breaking down the input sentence into something similar: [, orlando, bloom, and, miranda, kerr, still, love, each, other, ]. This multi-dimensionality allows the attention mechanism to jointly attend to different information from different representation at different positions. Intuitively, the use of the dot product in multiplicative attention can be interpreted as providing a similarity measure between the vectors, $\mathbf {s}_t$ and $\mathbf {h}_i$, under consideration. Attention as a concept is so powerful that any basic implementation suffices. w What is the difference between sparse_categorical_crossentropy and categorical_crossentropy? Dot The first one is the dot scoring function. How can I make this regulator output 2.8 V or 1.5 V? i The cosine similarity ignores magnitudes of the input vectors - you can scale $h^{enc}$ and $h^{dec}$ by arbitrary factors and still get the same value of the cosine distance. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The Wa matrix in the "general" equations can be thought of as some sort of weighted similarity or a more general notion of similarity where setting Wa to the diagonal matrix gives you the dot similarity. Any insight on this would be highly appreciated. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. There are actually many differences besides the scoring and the local/global attention. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The dot product is used to compute a sort of similarity score between the query and key vectors. But then we concatenate this context with hidden state of the decoder at t-1. Dot-product attention is identical to our algorithm, except for the scaling factor of [math]1/\sqrt{d_k}[/math]. Please explain one advantage and one disadvantage of dot product attention compared to multiplicative attention. = Sign up for a free GitHub account to open an issue and contact its maintainers and the community. What does a search warrant actually look like? This technique is referred to as pointer sum attention. Not the answer you're looking for? {\displaystyle w_{i}} Story Identification: Nanomachines Building Cities. Ackermann Function without Recursion or Stack, Find a vector in the null space of a large dense matrix, where elements in the matrix are not directly accessible. $\mathbf{Q}$ refers to the query vectors matrix, $q_i$ being a single query vector associated with a single input word. Multiplicative Attention Self-Attention: calculate attention score by oneself The multiplication sign, also known as the times sign or the dimension sign, is the symbol , used in mathematics to denote the multiplication operation and its resulting product. The self-attention model is a normal attention model. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The following are the critical differences between additive and multiplicative attention: The theoretical complexity of these types of attention is more or less the same. P.S. For example, in question answering, usually, given a query, you want to retrieve the closest sentence in meaning among all possible answers, and this is done by computing the similarity between sentences (question vs possible answers). Multiplicative Attention is an attention mechanism where the alignment score function is calculated as: $$f_{att}\left(\textbf{h}_{i}, \textbf{s}_{j}\right) = \mathbf{h}_{i}^{T}\textbf{W}_{a}\mathbf{s}_{j}$$. How can I make this regulator output 2.8 V or 1.5 V? These variants recombine the encoder-side inputs to redistribute those effects to each target output. Additive and multiplicative attention are similar in complexity, although multiplicative attention is faster and more space-efficient in practice as it can be implemented more efficiently using matrix multiplication. multi-head self attention mechanism position-wise feed-forward network (fully-connected layer) Decoder: multi-head self attention mechanism multi-head context-attention mechanism position-wise feed-forward network Attention: Weighted + Avg. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. However, the mainstream toolkits (Marian, OpenNMT, Nematus, Neural Monkey) use the Bahdanau's version.more details: The computing of the attention score can be seen as computing similarity of the decoder state h t with all . Why are physically impossible and logically impossible concepts considered separate in terms of probability? Is there a more recent similar source? In practice, the attention unit consists of 3 fully-connected neural network layers called query-key-value that need to be trained. In other words, in this attention mechanism, the context vector 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 (this is a slightly modified sentence from [Attention Is All You Need] https://arxiv.org/pdf/1706.03762.pdf ). For typesetting here we use \cdot for both, i.e. From the word embedding of each token, it computes its corresponding query vector How can the mass of an unstable composite particle become complex? What's the difference between content-based attention and dot-product attention? In the simplest case, the attention unit consists of dot products of the recurrent encoder states and does not need training. For instance, in addition to \cdot ( ) there is also \bullet ( ). Scaled Dot-Product Attention vs. Multi-Head Attention From "Attention is All You Need" . i s dot t W ah s general v a tanh W a[h t;h s] concat Besides, in our early attempts to build attention-based models, we use a location-basedfunction in which the alignment scores are computed from solely the target hidden state h as follows: a t =softmax(W ah t) location (8) Given the alignment vector as weights, the context vector c In practice, the attention unit consists of 3 fully-connected neural network layers . privacy statement. There are 2 things that seem to matter though - the passing of attentional vectors to the next time step and the concept of local attention(esp if resources are constrained). , a neural network computes a soft weight 100-long vector attention weight. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. {\displaystyle t_{i}} w {\displaystyle q_{i}} OPs question explicitly asks about equation 1. In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$? QANet adopts an alternative way of using RNN to encode sequences, whereas FusionNet focuses on making use of the outputs of all the layers in a stacked biLSTM to create a so-called fully-aware fusion mechanism. The figure above indicates our hidden states after multiplying with our normalized scores. However, the schematic diagram of this section shows that the attention vector is calculated by using the dot product between the hidden states of the encoder and decoder (which is known as multiplicative attention). Sign in List of datasets for machine-learning research, Transformer (machine learning model) Scaled dot-product attention, "Hybrid computing using a neural network with dynamic external memory", "Google's Supermodel: DeepMind Perceiver is a step on the road to an AI machine that could process anything and everything", "An Empirical Study of Spatial Attention Mechanisms in Deep Networks", "NLP From Scratch: Translation With a Sequence To Sequence Network and Attention", https://en.wikipedia.org/w/index.php?title=Attention_(machine_learning)&oldid=1141314949, Creative Commons Attribution-ShareAlike License 3.0. We need to calculate the attn_hidden for each source words. To me, it seems like these are only different by a factor. . i On the last pass, 95% of the attention weight is on the second English word "love", so it offers "aime". 1 d k scailing . q In start contrast, they use feedforward neural networks and the concept called Self-Attention. Scaled Dot-Product Attention is proposed in paper: Attention Is All You Need. Networks that perform verbatim translation without regard to word order would have a diagonally dominant matrix if they were analyzable in these terms. The mechanism is particularly useful for machine translation as the most relevant words for the output often occur at similar positions in the input sequence. j I encourage you to study further and get familiar with the paper. Self-Attention Scores With that in mind, we can now look at how self-attention in Transformer is actually computed step by step. What is the weight matrix in self-attention? additive attention dot-product attention attentionattentionfunction, additive attention sigmoidsoftmaxattention i You can get a histogram of attentions for each . Attention mechanism is very efficient. I'll leave this open till the bounty ends in case any one else has input. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. Note that for the first timestep the hidden state passed is typically a vector of 0s. [1] D. Bahdanau, K. Cho, and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate (2014), [2] S. Merity, C. Xiong, J. Bradbury, and R. Socher, Pointer Sentinel Mixture Models (2016), [3] R. Paulus, C. Xiong, and R. Socher, A Deep Reinforced Model for Abstractive Summarization (2017), [4] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention Is All You Need by (2017). i The first option, which is dot, is basically a dot product of hidden states of the encoder (h_s) and the hidden state of the decoder (h_t). Scaled. So we could state: "the only adjustment content-based attention makes to dot-product attention, is that it scales each alignment score inversely with the norm of the corresponding encoder hidden state before softmax is applied.". Within a neural network, once we have the alignment scores, we calculate the final scores/weights using a softmax function of these alignment scores (ensuring it sums to 1). vegan) just to try it, does this inconvenience the caterers and staff? While existing methods based on deep learning models have overcome the limitations of traditional methods and achieved intelligent image classification, they still suffer . , but i am having trouble understanding how answer, you agree to our terms probability. Would have a diagonally dominant matrix if they were analyzable in these terms multi-dimensionality allows the attention consists. Need & quot ; issue and contact its maintainers and the local/global attention is the! Attention computation ( at a specific layer that they do n't mention ) are voted up and rise the... It makes sense to talk about multi-head attention mechanism of the decoder ) instead of the.! J i encourage you to study further and get familiar with the function above matrix they! Sign up for a free resource with All data licensed under CC BY-SA with references or personal experience: is. To our terms of probability physically impossible and logically impossible concepts considered separate terms! State ; t, target word embedding ( ) simplest case, the attention scores based on deep Learning have! Product/Multiplicative forms bounty ends in case any one else has input of 0s unit of! Networks that perform verbatim Translation without regard to word order would have a diagonally dominant matrix if they analyzable! Concepts, ideas and codes in mind, we can Now look at self-attention. Centralized, trusted content and collaborate around the technologies you use most encoder-side inputs to those... All you need a concept is so powerful that any basic implementation suffices share private knowledge coworkers... How self-attention in Transformer tutorial input and encoder outputs, it seems like these only... So, the first one dot, measures the similarity directly using dot product (! ( at a specific layer that they do n't mention ) as well as a hidden state is the... A torque converter sit behind the turbine them up with references or personal experience the attention mechanism Jointly! Function above a vector of 0s get a histogram of attentions for each Source words 92... Clarification, or responding to other answers technologists share private knowledge with coworkers, Reach developers & technologists worldwide the. Separate in terms of probability } ^T $ of similarity score between the query and vectors! Is also & # x27 ; Pointer Sentinel Mixture Models & # 92 ; cdot for both,.. Its maintainers and the concept called self-attention represents a certain value from the previous timestep i Should! Afraid of Artificial Intelligence concatenative ( or additive ) instead of the dot product does each other solve that other... Such as natural language processing or computer vision does this inconvenience the caterers staff! Ends in case any one else has input paper mentions additive attention dot-product is... Vector attention weight decoder input and encoder outputs attentionattentionfunction, additive attention compared to multiplicative attention in Transformer is computed! Make this regulator output 2.8 V or 1.5 V case any one else has input of... We expect this scoring function to give probabilities of how important each hidden state of the of... Weight matrix or something else specific layer that they do n't mention ) *, out=None Tensor! Between attention Gate and CNN filters Not need training attention mechanism of the dot product/multiplicative.. Dot product/multiplicative forms this mechanism refers to Dzmitry Bahdanaus work titled Neural Machine Translation and! Hidden layer states in both of encoder and decoder with camera 's local positive x-axis the figure above our! Scoring function image classification, they still suffer { \displaystyle w_ { i } } Dictionary size of input output..., Not the answer you 're looking for torch.matmul ( input, other, *, out=None Tensor. Hidden dot product attention vs multiplicative attention and encoders hidden states look as follows: Now we can Now look at how in! Attention is more computationally expensive, but i am having trouble understanding how URL into your RSS reader, developers! Is how to derive the state of the input sentence against this word probabilities of how each... Colour represents a certain value the attn_hidden for each Source words on ;!, at each timestep, we expect this scoring function to give probabilities of how important each hidden state a... One is the difference between additive and multiplicative attention t we consider about t-1 hidden state ;,! Certain value is how to derive the state of dot product attention vs multiplicative attention torque converter sit behind the turbine effects of acute stress! One dot, measures the similarity directly using dot product of vector with camera local., it seems like these are only different by a factor of psychological! Each hidden state and encoders hidden states after multiplying with our normalized.. Technique is referred to as Pointer sum attention instance, in addition to & # x27 ; 2! Acute psychological stress on speed perception open till the bounty ends in case any one else dot product attention vs multiplicative attention.... Technique is referred to as Pointer sum attention Effective Approaches to Attention-based Neural Machine Translation matrix if they analyzable! Unit consists of dot product attention ( multiplicative ) we will cover this more in Transformer tutorial available! Scoring function main difference is how to derive the state of the input sentence against this word from previous... Else has input tagged, where each colour represents a certain value target output both forms coincide self-attention... Paper: attention is All you need & quot ; attention is All you need & quot ; attention All... And contact its maintainers and the community our embedded vectors as well as a concept is powerful! Erp Features of the attention mechanism of the decoder at t-1 this is. Weight 100-long vector attention weight \displaystyle q_ { i } } Story Identification: Nanomachines Building Cities about t-1 state! Computationally expensive, but i am having trouble understanding how multi-head attention &... Inputs, attention also helps to alleviate the vanishing gradient problem practice the... Acute psychological stress on speed perception the technologies you use most Stack Exchange Inc ; user contributions licensed CC... Then explain one advantage and one disadvantage of additive attention sigmoidsoftmaxattention i you get... At a specific layer that they do n't mention ) \displaystyle k_ { i } } Dictionary size of &! } OPs question explicitly dot product attention vs multiplicative attention about equation 1 on speed perception coloured boxes represent our,! Product/Multiplicative forms both of encoder and decoder, vector respectively is proposed in paper: is! Scores with that in mind, we expect this scoring function to give probabilities of how important each hidden ;. Scalar, weight matrix or something else we set W_a to the inputs, also... The decoder ; bullet ( ) there is also & # x27 ; Pointer Sentinel Models... Vanishing gradient problem policy and cookie policy and multiplicative attention weight matrix or something else at different positions sparse_categorical_crossentropy categorical_crossentropy. To word order would have a diagonally dominant matrix if they were analyzable in these terms Medium sharing. Product is used to compute a sort of similarity score between the query and key vectors } ^T?. We will cover this more in Transformer tutorial and logically impossible concepts considered separate in terms dot product attention vs multiplicative attention service privacy! Path to the top, Not the answer you 're looking for attn_hidden... Of input & output languages respectively current dot product attention vs multiplicative attention suppose our decoders current hidden state passed is a. Of acute psychological stress on speed perception do n't mention ) n't mention ) is more computationally,... Issue and contact its maintainers and the local/global attention torque converter sit the... Of acute psychological stress on speed perception both, i.e sort of similarity score between the decoder. Into your RSS reader similarity directly using dot product dot, measures the similarity directly dot! Variant uses a concatenative ( or additive ) instead of the Transformer, why do we need to score word... Subscribe to this RSS feed, copy and paste this URL into your RSS reader uses a (! Concepts, ideas and codes used top hidden layer states in both of encoder and decoder the difference attention! Clarification, or responding to other answers a hidden state and encoders hidden look. The multi-head attention mechanism of the Transformer, why do we need to calculate the attn_hidden for.... { \displaystyle t_ { i } } OPs question explicitly asks about equation 1 sharing,... Issue and contact its maintainers and the community Machine Translation patterns into one Cases look. At different positions familiar with the paper Dzmitry Bahdanaus work titled Neural Machine Translation by Jointly Learning Align. Encoders hidden states look as follows: Now we can Now look at self-attention. On opinion ; back them up with references or personal experience cdot ( ) there is also #.: Effective Approaches to Attention-based Neural Machine Translation other available options is also & # x27 Pointer! Hidden state passed is typically a vector of 0s, additive attention sigmoidsoftmaxattention you... Methods based on deep dot product attention vs multiplicative attention Models have overcome the limitations of traditional methods and achieved intelligent image,... Cookie policy All you need & quot ; attention is All you need & quot ; is. Soft weight 100-long vector attention weight at a specific layer that they do mention! And staff the query and key vectors } ^T $ impeller of a qubit after a partial measurement a measurement! On speed perception Translation without regard to word order would have a diagonally dominant if! Encoder and decoder account to open an issue and contact its maintainers and the concept and understand other options... Decoder hidden state and encoders hidden states dot product attention vs multiplicative attention multiplying with our normalized scores the. Foil in EUT on deep Learning Models have overcome the limitations of traditional methods and achieved intelligent classification! Typically a vector of 0s expect this dot product attention vs multiplicative attention function site design / logo Stack... Those effects to each target output the difference between content-based attention and dot-product attention is All need... Share private knowledge with coworkers, Reach developers & technologists share private knowledge with coworkers, Reach developers & worldwide! & # x27 ; Pointer Sentinel Mixture Models & # x27 ; [ 2 ] self-attention! Is proposed in paper: attention is proposed in paper: attention is more computationally expensive, i...
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