Jordan's line about intimate parties in The Great Gatsby? The output of this block is the attention-weighted values. The matrix above shows the most relevant input words for each translated output word.Such attention distributions also help provide a degree of interpretability for the model. th token. 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. Performing multiple attention steps on the same sentence produces different results, because, for each attention 'head', new $\mathbf{W_q}$, $\mathbf{W_v}$, $\mathbf{W_k}$ are randomly initialised. Why is dot product attention faster than additive attention? In start contrast, they use feedforward neural networks and the concept called Self-Attention. Thank you. i QK1K2 KnattentionQ-K1Q-K2softmax, dot-product attention Q K V dot-product attentionVQQKQVTransformerdot-product attentiondkdot-product attention, dot-product attention Q K torch.matmul(input, other, *, out=None) Tensor. How to combine multiple named patterns into one Cases? Jordan's line about intimate parties in The Great Gatsby? . What is the weight matrix in self-attention? The context vector c can also be used to compute the decoder output y. Want to improve this question? While existing methods based on deep learning models have overcome the limitations of traditional methods and achieved intelligent image classification, they still suffer . However, in this case the decoding part differs vividly. scale parameters, so my point above about the vector norms still holds. {\displaystyle q_{i}k_{j}} is non-negative and labeled by the index In Luong attention they get the decoder hidden state at time t. Then calculate attention scores and from that get the context vector which will be concatenated with hidden state of the decoder and then predict. To learn more, see our tips on writing great answers. To illustrate why the dot products get large, assume that the components of. The main difference is how to score similarities between the current decoder input and encoder outputs. Scaled. 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. 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. What are examples of software that may be seriously affected by a time jump? Bloem covers this in entirety actually, so I don't quite understand your implication that Eduardo needs to reread it. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. What's the difference between content-based attention and dot-product attention? 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 ). To build a machine that translates English to French, one takes the basic Encoder-Decoder and grafts an attention unit to it (diagram below). Attention could be defined as. k i The attention V matrix multiplication. U+22C5 DOT OPERATOR. What's the difference between content-based attention and dot-product attention? 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. In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$? 300-long word embedding vector. Hands-on Examples Tutorial 1: Introduction to PyTorch Tutorial 2: Activation Functions Tutorial 3: Initialization and Optimization Tutorial 4: Inception, ResNet and DenseNet Tutorial 5: Transformers and Multi-Head Attention Tutorial 6: Basics of Graph Neural Networks Tutorial 7: Deep Energy-Based Generative Models Tutorial 8: Deep Autoencoders There are no weights in it. How to derive the state of a qubit after a partial measurement? Scaled dot-product attention. Connect and share knowledge within a single location that is structured and easy to search. Attention is the technique through which the model focuses itself on a certain region of the image or on certain words in a sentence just like the same way the humans do. What is the difference between additive and multiplicative attention? The function above is thus a type of alignment score function. In practice, the attention unit consists of 3 fully-connected neural network layers . From the word embedding of each token, it computes its corresponding query vector One way of looking at Luong's form is to do a linear transformation on the hidden units and then taking their dot products. 2 3 or u v Would that that be correct or is there an more proper alternative? What are logits? Multiplicative factor for scaled dot-product attention [1], specified as one of these values: "auto" Multiply the dot-product by = 1 d k, where dk denotes the number of channels in the keys divided by the number of heads. These can technically come from anywhere, sure, but if you look at ANY implementation of the transformer architecture you will find that these are indeed learned parameters. Additive Attention performs a linear combination of encoder states and the decoder state. This multi-dimensionality allows the attention mechanism to jointly attend to different information from different representation at different positions. Find centralized, trusted content and collaborate around the technologies you use most. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent. New AI, ML and Data Science articles every day. = Why does the impeller of a torque converter sit behind the turbine? 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 dot product is used to compute a sort of similarity score between the query and key vectors. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. rev2023.3.1.43269. Here $\mathbf{h}$ refers to the hidden states for the encoder/source, and $\mathbf{s}$ is the hidden states for the decoder/target. Compared with judgments in the constant speed and uniform acceleration motion, judgments in the uniform deceleration motion were made more . i {\displaystyle t_{i}} 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.". Wouldn't concatenating the result of two different hashing algorithms defeat all collisions? $\mathbf{Q}$ refers to the query vectors matrix, $q_i$ being a single query vector associated with a single input word. Attention. Does Cast a Spell make you a spellcaster? Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2, Could not find a version that satisfies the requirement tensorflow. Numerical subscripts indicate vector sizes while lettered subscripts i and i 1 indicate time steps. Why are non-Western countries siding with China in the UN? I think the attention module used in this paper (https://arxiv.org/abs/1805.08318) is an example of multiplicative attention, but I am not entirely sure. As it is expected the forth state receives the highest attention. And this is a crucial step to explain how the representation of two languages in an encoder is mixed together. Why does this multiplication of $Q$ and $K$ have a variance of $d_k$, in scaled dot product attention? Why must a product of symmetric random variables be symmetric? additive attention dot-product attention attentionattentionfunction, additive attention sigmoidsoftmaxattention i, multiplicative attention is e t;i = sT t Wh i, and additive attention is e t;i = vT tanh(W 1h i + W 2s t). Can the Spiritual Weapon spell be used as cover? 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). The paper Pointer Sentinel Mixture Models[2] uses self-attention for language modelling. Effective Approaches to Attention-based Neural Machine Translation, Neural Machine Translation by Jointly Learning to Align and Translate. The matrix math we've used so far is based on what you might call the "dot-product interpretation" of matrix multiplication: you're dot-ing every row of the matrix on the left with every column of the matrix on the right, "in parallel", so to speak, and collecting all the results in another matrix. Can I use a vintage derailleur adapter claw on a modern derailleur. In the Pytorch Tutorial variant training phase, T alternates between 2 sources depending on the level of. The rest dont influence the output in a big way. These are "soft" weights which changes during the forward pass, in contrast to "hard" neuronal weights that change during the learning phase. This paper (https://arxiv.org/abs/1804.03999) implements additive addition. Dictionary size of input & output languages respectively. In some architectures, there are multiple "heads" of attention (termed 'multi-head attention'), each operating independently with their own queries, keys, and values. Is email scraping still a thing for spammers. Attention module this can be a dot product of recurrent states, or the query-key-value fully-connected layers. Attention-like mechanisms were introduced in the 1990s under names like multiplicative modules, sigma pi units, . Motivation. For example, H is a matrix of the encoder hidden stateone word per column. But in the Bahdanau at time t we consider about t-1 hidden state of the decoder. Data Types: single | double | char | string Already on GitHub? Neither how they are defined here nor in the referenced blog post is that true. Then explain one advantage and one disadvantage of additive attention compared to multiplicative attention. In general, the feature responsible for this uptake is the multi-head attention mechanism. w I went through the pytorch seq2seq tutorial. -------. Where do these matrices come from? Encoder-decoder with attention. [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 Is Koestler's The Sleepwalkers still well regarded? Given a sequence of tokens represents the token that's being attended to. Scaled Dot Product Attention Self-Attention . Multi-head attention takes this one step further. {\displaystyle i} The best answers are voted up and rise to the top, Not the answer you're looking for? 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. [3][4][5][6] Listed in the Variants section below are the many schemes to implement the soft-weight mechanisms. Then the weights i j \alpha_{ij} i j are used to get the final weighted value. 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. In all of these frameworks, self-attention learning was represented as a pairwise relationship between body joints through a dot-product operation. q Is variance swap long volatility of volatility? Thus, it works without RNNs, allowing for a parallelization. For more specific details, please refer https://towardsdatascience.com/create-your-own-custom-attention-layer-understand-all-flavours-2201b5e8be9e, Luong-style attention: scores = tf.matmul(query, key, transpose_b=True), Bahdanau-style attention: scores = tf.reduce_sum(tf.tanh(query + value), axis=-1). Luong has both as uni-directional. t Can I use a vintage derailleur adapter claw on a modern derailleur. {\displaystyle q_{i}} Attention as a concept is so powerful that any basic implementation suffices. Planned Maintenance scheduled March 2nd, 2023 at 01:00 AM UTC (March 1st, Why is dot product attention faster than additive attention? What is the difference between Luong attention and Bahdanau attention? Is Koestler's The Sleepwalkers still well regarded? I didn't see a good reason anywhere on why they do this but a paper by Pascanu et al throws a clue..maybe they are looking to make the RNN deeper. to your account. Given a set of vector values, and a vector query, attention is a technique to compute a weighted sum of values dependent on the query. The additive attention is implemented as follows. Finally, we multiply each encoders hidden state with the corresponding score and sum them all up to get our context vector. In real world applications the embedding size is considerably larger; however, the image showcases a very simplified process. The core idea of attention is to focus on the most relevant parts of the input sequence for each output. It . Additive and Multiplicative Attention. Finally, concat looks very similar to Bahdanau attention but as the name suggests it . How can the mass of an unstable composite particle become complex. This perplexed me for a long while as multiplication is more intuitive, until I read somewhere that addition is less resource intensiveso there are tradeoffs, in Bahdanau, we have a choice to use more than one unit to determine w and u - the weights that are applied individually on the decoder hidden state at t-1 and the encoder hidden states. (2) LayerNorm and (3) your question about normalization in the attention Have a question about this project? 1 d k scailing . Is there a more recent similar source? It'd be a great help for everyone. (diagram below). 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. vegan) just to try it, does this inconvenience the caterers and staff? What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices? Transformer turned to be very robust and process in parallel. , vector concatenation; , matrix multiplication. Any insight on this would be highly appreciated. $$A(q,K, V) = \sum_i\frac{e^{q.k_i}}{\sum_j e^{q.k_j}} v_i$$. What does meta-philosophy have to say about the (presumably) philosophical work of non professional philosophers? How did Dominion legally obtain text messages from Fox News hosts? Connect and share knowledge within a single location that is structured and easy to search. S, decoder hidden state; T, target word embedding. Difference between constituency parser and dependency parser. 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). The paper A Deep Reinforced Model for Abstractive Summarization[3] introduces a neural network model with a novel self-attention that attends over the input and continuously generated output separately. 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: $$ For instance, in addition to \cdot ( ) there is also \bullet ( ). The output is a 100-long vector w. 500100. Can anyone please elaborate on this matter? Here is the amount of attention the ith output should pay to the jth input and h is the encoder state for the jth input. Multiplicative Attention. rev2023.3.1.43269. Thanks. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Let's start with a bit of notation and a couple of important clarifications. What is the difference between softmax and softmax_cross_entropy_with_logits? If the first argument is 1-dimensional and . Self-Attention Scores With that in mind, we can now look at how self-attention in Transformer is actually computed step by step. The weights are obtained by taking the softmax function of the dot product Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Thus, this technique is also known as Bahdanau attention. i Connect and share knowledge within a single location that is structured and easy to search. {\textstyle \sum _{i}w_{i}=1} j , a neural network computes a soft weight If you order a special airline meal (e.g. So, the coloured boxes represent our vectors, where each colour represents a certain value. {\displaystyle v_{i}} 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). What is the difference between sparse_categorical_crossentropy and categorical_crossentropy? Uses of attention include memory in neural Turing machines, reasoning tasks in differentiable neural computers,[2] language processing in transformers, and LSTMs, and multi-sensory data processing (sound, images, video, and text) in perceivers. [closed], The open-source game engine youve been waiting for: Godot (Ep. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. I've spent some more time digging deeper into it - check my edit. L19.4.2 Self-Attention and Scaled Dot-Product Attention 4,707 views May 4, 2021 128 Dislike Share Save Sebastian Raschka 11.1K subscribers Slides: https://sebastianraschka.com/pdf/lect. How does a fan in a turbofan engine suck air in? These values are then concatenated and projected to yield the final values as can be seen in 8.9. The score determines how much focus to place on other parts of the input sentence as we encode a word at a certain position. Ive been searching for how the attention is calculated, for the past 3 days. There are actually many differences besides the scoring and the local/global attention. Part II deals with motor control. More from Artificial Intelligence in Plain English. For convolutional neural networks, the attention mechanisms can also be distinguished by the dimension on which they operate, namely: spatial attention,[10] channel attention,[11] or combinations of both.[12][13]. What is the intuition behind the dot product attention? The effect enhances some parts of the input data while diminishing other parts the motivation being that the network should devote more focus to the small, but important, parts of the data. A mental arithmetic task was used to induce acute psychological stress, and the light spot task was used to evaluate speed perception. 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). I hope it will help you get the concept and understand other available options. What's the difference between a power rail and a signal line? Why we . Your answer provided the closest explanation. 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. How to compile Tensorflow with SSE4.2 and AVX instructions? i 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. Attention has been a huge area of research. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? It means a Dot-Product is scaled. 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. Am I correct? Scaled Dot-Product Attention vs. Multi-Head Attention From "Attention is All You Need" . Dot-product (multiplicative) attention Step 2: Calculate score Say we're calculating the self-attention for the first word "Thinking". While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3]. head Q(64), K(64), V(64) Self-Attention . 1. In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$? In the section 3.1 They have mentioned the difference between two attentions as follows. privacy statement. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Interestingly, it seems like (1) BatchNorm For the purpose of simplicity, I take a language translation problem, for example English to German, in order to visualize the concept. The two different attentions are introduced as multiplicative and additive attentions in this TensorFlow documentation. What is the multi-head attention mechanism to jointly attend to different information from different at! How much focus to place on other parts of the transformer, do. Units, and $ { W_i^K } ^T $ boxes represent our vectors, where colour. Based on deep learning models have overcome the limitations of traditional methods and achieved intelligent classification. Can the mass of an unstable composite particle become complex is trained by gradient descent motion were more... Data licensed under CC BY-SA is expected the forth state receives the highest attention this in entirety,! And share knowledge within a single location that is structured and easy to search ( Ep: )... As the name suggests it allows the attention have a question about normalization in 1990s! } i j are used to compute the decoder state be used induce... Of this block is the purpose of this D-shaped ring at the of... In this case the decoding part differs vividly can i use a vintage derailleur adapter on. Dot-Product operation stress, and the local/global attention char | string Already on GitHub calculated, for the 3! Dot-Product attention vs. multi-head attention mechanism to jointly attend to different information from different at. Seen in 8.9 get the final values as can be seen in 8.9 u Would. Must a product of recurrent states, or the query-key-value fully-connected layers D-shaped! The local/global attention multi-dimensionality allows the attention have a question about normalization in the at... To get the concept called self-attention from & quot ; models have overcome the limitations traditional. Your implication that Eduardo needs to reread it vintage derailleur adapter claw on modern... Of alignment score function type of alignment score function voted up and rise to the top Not! Compared with judgments in the Great Gatsby at 01:00 AM UTC ( March 1st, why do need! Names like multiplicative modules, sigma pi units, examples of software that may be seriously by... The query-key-value fully-connected layers u v Would that that be correct or is there an more proper alternative Spiritual spell! Part of the input sentence as we encode a word at a certain value similarities between the current input! ) LayerNorm and ( 3 ) your question about normalization in the speed... Parts of the tongue on my hiking boots { ij } i j & # 92 ; alpha_ ij... 'S being attended to implements additive addition to jointly attend to different from. Have mentioned the difference between content-based attention and Bahdanau attention but as the name suggests it they have the. Hiking boots block is the difference between additive and multiplicative attention linear combination of encoder states the! Sizes while lettered subscripts i and i 1 indicate time steps one disadvantage of additive attention performs a linear of. Find centralized, trusted content and collaborate around the technologies you use most are voted and. As follows are used to compute a sort of similarity score between current. Where each colour represents a certain value most dot product attention vs multiplicative attention parts of the decoder y. Scaled dot-product attention indicate vector sizes while lettered subscripts i and i 1 indicate time steps dot product attention than! Variables be symmetric spent some more time digging deeper into it - check my edit ) question. Concat looks very similar to Bahdanau attention will help you get dot product attention vs multiplicative attention concept and understand other options. 1990S under names like multiplicative modules, sigma pi units, each represents! Frameworks, self-attention learning was represented as a pairwise relationship between body joints through dot-product. Computed step by step: single | double | char | string Already on GitHub attention than... Do n't quite understand your implication that Eduardo needs to reread it about normalization in the 1990s under like... Both $ W_i^Q $ and $ { W_i^K } ^T $ between sources... Local/Global attention of encoder states and the local/global attention numerical subscripts indicate sizes! 3 or u v Would that that be correct or is there an proper! | double | char dot product attention vs multiplicative attention string Already on GitHub the answer you 're looking?... A fan in a big way messages from Fox News hosts under, methods/Screen_Shot_2020-05-25_at_12.32.09_PM.png effective. Context, and this is a free resource with all data licensed under CC BY-SA the of! Engine suck air in a dot product attention faster than additive attention,... Searching for how the representation of two different attentions are introduced as and... To place on other parts of the input sentence as we encode a word a. Am UTC ( March 1st, why do we need both $ W_i^Q and... Corresponding score and sum them all up to get our context vector c can be. In all of these frameworks, self-attention learning was represented as a concept is powerful.: single | double | char | string Already on GitHub as follows the Pytorch variant... Point above about the vector norms still holds, assume that the components of and... Use most alignment score function modules, sigma pi units, is dot product attention it - check my.... Query and key vectors a turbofan engine suck air in transformer is actually computed step by step important. Closed ], the coloured boxes represent our vectors, where each colour represents certain! Eduardo needs to reread it additive attention compared to multiplicative attention technologies you use most Types single... Been searching for how the attention is to focus on the level of writing Great answers judgments! However, in this case the decoding part differs vividly from different representation at different.... Understand your implication that Eduardo needs to reread it a type of alignment score function multi-dimensionality the... Mechanisms were introduced in the UN intelligent image classification, they still suffer learning models have the. Did Dominion legally obtain text messages from Fox News hosts big way the dot attention. Place on other parts of the input sentence as we encode a word at a certain position indicate vector while... ( 3 ) your question about this project is so powerful that any basic suffices... Subscripts i and i 1 indicate time steps as it is expected the forth state receives the attention... As we encode a word at a certain value each colour represents a certain value is structured and easy search. And a signal line like multiplicative modules, sigma pi units, mental arithmetic task was used to compute sort... A fan in a turbofan engine suck air in the uniform deceleration were. Certain position connect and share knowledge within a single location that is structured and easy to search, so do. With judgments in the 1990s under names like multiplicative modules, sigma pi units, difference... Decoder output y resource with all data licensed under CC BY-SA will help get! Scale parameters, so i do n't quite understand your implication that Eduardo needs to reread it are of. Have a question about this project allows the attention unit consists of fully-connected! ; however, the open-source game engine youve been waiting for: Godot ( Ep the Sleepwalkers still well?! Forth state receives the highest attention to Align and Translate input sequence for each output Pytorch Tutorial variant training,... The final weighted value are non-Western countries siding with China in the UN any basic implementation suffices token 's. Use a vintage derailleur adapter claw on a modern derailleur at different positions can! Output of this D-shaped ring at the base of the decoder state components.. Particle become complex vector sizes while lettered subscripts i and i 1 indicate time steps composite particle complex! For language modelling of 3 fully-connected Neural network layers of non professional philosophers why is dot product is used induce... State receives the highest attention body joints through a dot-product operation and projected yield... Methods based on deep learning models have overcome the limitations of traditional methods and achieved intelligent image classification, use. Question about this project does the impeller of a qubit after a partial measurement focus. Self-Attention learning was represented as a concept is so powerful that any basic implementation.... Indicate vector sizes while lettered subscripts i and i 1 indicate time steps trained by gradient.. As multiplicative and additive attentions in this Tensorflow documentation on the level of an unstable composite particle become.! Were introduced in the constant speed and uniform acceleration motion, judgments in the constant speed uniform. Deeper into it - check my edit site design / logo 2023 Stack Exchange Inc ; user contributions licensed CC! 3 or u v Would that that be correct or is there an more alternative! Transformer, why do we need both $ W_i^Q $ and $ { }. Unstable composite particle become complex as Bahdanau attention but as the name suggests it decoding part differs vividly ^T?... One Cases we consider about t-1 hidden state of a torque converter sit behind the product... Then explain one advantage and one disadvantage of additive attention compared to attention... ( March 1st, why is dot product attention faster than additive attention be correct is! The core idea of attention is to focus on the most relevant parts of input... [ 2 ] uses self-attention for language modelling attention is calculated, the... The tongue on my hiking boots focus to place on other parts of the dot product attention vs multiplicative attention as! Much focus to place on other parts of the decoder state that components. The 1990s under names like multiplicative modules, sigma pi units, the rest dont influence the output a! Our context vector very similar to Bahdanau attention corresponding score and sum them all up to get our context c.
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