bahdanau attention explained

# bahdanau attention explained

score_{alignment} = H_{encoder} \cdot H_{decoder}, score_{alignment} = W(H_{encoder} \cdot H_{decoder}), score_{alignment} = W \cdot tanh(W_{combined}(H_{encoder} + H_{decoder})). Additive/concat and dot product have been mentioned in this article. Pro: the model is smooth and differentiable. al (2014) and Cho. The first type of Attention, commonly referred to as Additive Attention, came from a paper by Dzmitry Bahdanau, which explains the less-descriptive original name. Step 2: Run all the scores through a softmax layer. In Luong Attention, there are three different ways that the alignment scoring function is defined- dot, general and concat. Luong et al., 2015’s Attention Mechanism. In their earliest days, Attention Mechanisms were used primarily in the field of visual imaging, beginning in about the 1990s. The encoder is a bidirectional (forward+backward) gated recurrent unit (BiGRU). This paragraph has 100 words. My mission is to convert an English sentence to a German sentence using Bahdanau Attention. This is because it enables the model to “remember” all the words in the input and focus on specific words when formulating a response. This means we can expect that the first translated word should match the input word with the [5, 0, 1] embedding. al, 2014), [6] TensorFlow’s seq2seq Tutorial with Attention (Tutorial on seq2seq+attention), [7] Lilian Weng’s Blog on Attention (Great start to attention), [8] Jay Alammar’s Blog on Seq2Seq with Attention (Great illustrations and worked example on seq2seq+attention), [9] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (Wu et. Attention: Examples3. [1] Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et. The pioneers of NMT are proposals from Kalchbrenner and Blunsom (2013), Sutskever et. Comparison to (Bahdanau et al., 2015) –While our global attention approach is similar in spirit to the model proposed by Bahdanau et al. At each time step of the decoder, we have to calculate the alignment score of each encoder output with respect to the decoder input and hidden state at that time step. In the above example, we obtain a high attention score of 60 for the encoder hidden state [5, 0, 1]. In seq2seq, the idea is to have two recurrent neural networks (RNNs) with an encoder-decoder architecture: read the input words one by one to obtain a vector representation of a fixed dimensionality (encoder), and, conditioned on these inputs, extract the output words one by one using another RNN (decoder). It is possible that if the sentence is extremely long, he might have forgotten what he has read in the earlier parts of the text. Intuition: seq2seqA translator reads the German text from start till the end. Intuition: seq2seq with 2-layer stacked encoder + attention. If you’re using FloydHub with GPU to run this code, the training time will be significantly reduced. The second type of Attention was proposed by Thang Luong in this paper. Using our trained model, let’s visualise some of the outputs that the model produces and the attention weights the model assigns to each input element. The score function in the attention layer is the. Bahdanau Attention is also known as Additive attention as it performs a linear combination of encoder states and the decoder states. Once everyone is done reading this English text, Translator A is told to translate the first word. Attention mechanisms revolutionized machine learning in applications ranging from NLP through computer vision to reinforcement learning. Google’s BERT, OpenAI’s GPT and the more recent XLNet are the more popular NLP models today and are largely based on self-attention and the Transformer architecture. Explanation adapted from [5]. The authors of Effective Approaches to Attention-based Neural Machine Translation have made it a point to simplify and generalise the architecture from Bahdanau et. The concatenation between output from current decoder time step, and context vector from the current time step are fed into a feed-forward neural network to give the final output (pink) of the current decoder time step. Modelling Bahdanau Attention using Election methods aided by Q-Learning. Special thanks to Derek, William Tjhi, Yu Xuan, Ren Jie, Chris, and Serene for ideas, suggestions and corrections to this article. In tensorflow-tutorials-for-text they are implementing bahdanau attention layer to generate context vector by giving encoder inputs, decoder hidden states and decoder inputs.. Encoder class is simply passing the encoder inputs from Embedding layer to GRU layer along with encoder_states and returns encoder_outputs and ecoder_states. To integrate context vector c→t, Bahdanau attention chooses to concatenate it with hidden state h→t−1 as the new hidden state which is fed to next step to generate h… At every word, Translator A shares his/her findings with Translator B, who will improve it and share it with Translator C — repeat this process until we reach Translator H. Also, while reading the German text, Translator H writes down the relevant keywords based on what he knows and the information he has received. The Attention mechanism in Deep Learning is based off this concept of directing your focus, and it pays greater attention to certain factors when processing the data. Distilling knowledge from Neural Networks to build smaller and faster models, Between the input and output elements (General Attention), Within the input elements (Self-Attention), The way that the alignment score is calculated, The position at which the Attention mechanism is being introduced in the decoder, Tokenizing the sentences and creating our vocabulary dictionaries, Assigning each word in our vocabulary to integer indexes, Converting our sentences into their word token indexes. Attention is the key innovation behind the recent success of Transformer-based language models such as BERT. After obtaining all of our encoder outputs, we can start using the decoder to produce outputs. Say we have the sentence “How was your day”, which we would like to translate to the French version - “Comment se passe ta journée”. This is a hands-on description of these models, using the DyNet framework. Here’s a quick intuition on this model. The type of attention that uses all the encoder hidden states is also known as global attention. It does this by creating a unique mapping between each time step of the decoder output to all the encoder hidden states. Feel free to visit my website at remykarem.github.io. Additive Attention, also known as Bahdanau Attention, uses a one-hidden layer feed-forward network to calculate the attention alignment score: f a t t ( h i, s j) = v a T tanh. Thereafter, they will be added together before being passed through a tanh activation function. In Luong attention they get the decoder hidden state at time t . It is advised that you have some knowledge of Recurrent Neural Networks (RNNs) and their variants, or an understanding of how sequence-to-sequence models work. ‪Element AI‬ - ‪Cited by 33,644‬ - ‪Artificial Intelligence‬ - ‪Machine Learning‬ - ‪Deep Learning‬ While Attention does have its application in other fields of deep learning such as Computer Vision, its main breakthrough and success comes from its application in Natural Language Processing (NLP) tasks. Effective Approaches to Attention-based Neural Machine Translation - Luong 에 대한 리뷰입니다. ∙ IIT Kharagpur ∙ 0 ∙ share . We will be using English to German sentence pairs obtained from the Tatoeba Project, and the compiled sentences pairs can be found at this link. The context vector we produced will then be concatenated with the previous decoder output. The decoder also has the same architecture, whose initial hidden states are the last encoder hidden states. We will only cover the more popular adaptations here, which are its usage in sequence-to-sequence models and the more recent Self-Attention. Implements Bahdanau-style (additive) attention. 11/10/2019 ∙ by Rakesh Bal, et al. When the input and output embeddings are the same across different layers, the memory is identical to the attention mechanism of Bahdanau. The class BahdanauDecoderLSTM defined below encompasses these 3 steps in the forward function. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio Neural machine translation is a recently proposed approach to machine translation. We covered the early implementations of Attention in seq2seq models with RNNs in this article. al. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. In this example, the score function is a dot product between the decoder and encoder hidden states. Nevertheless, this process acts as a sanity check to ensure that our model works and is able to function end-to-end and learn. Lastly, the resultant vector from the previous few steps will undergo matrix multiplication with a trainable vector, obtaining a final alignment score vector which holds a score for each encoder output. With this setting, the model is able to selectively focus on useful parts of the input sequence and hence, learn the alignment between them. Hard Attention: only selects one patch of the image to attend to at a time. 0.1), a vector representation which is like a numerical summary of an input sequence. al (2015) [ 1] This implementation of attention is one of the founding attention fathers. activation_gelu: Gelu activation_hardshrink: Hardshrink activation_lisht: Lisht activation_mish: Mish activation_rrelu: Rrelu activation_softshrink: Softshrink activation_sparsemax: Sparsemax activation_tanhshrink: Tanhshrink attention_bahdanau: Bahdanau Attention attention_bahdanau_monotonic: Bahdanau Monotonic Attention Summary of the Code. We’ll be testing the LuongDecoder model with the scoring function set as concat. Step 1: Obtain a score for every encoder hidden state. SummaryAppendix: Score Functions. There are 2 types of attention, as introduced in [2]. Soft Attention: the alignment weights are learned and placed “softly” over all patches in the source image; essentially the same type of attention as in Bahdanau et al., 2015. In the illustration above, the hidden size is 3 and the number of encoder outputs is 2. The code implementation and some calculations in this process is different as well, which we will go through now. Attention places different focus on different words by assigning each word with a score. al (2014b), where the more familiar framework is the sequence-to-sequence (seq2seq) learning from Sutskever et. The alignment scores for Bahdanau Attention are calculated using the hidden state produced by the decoder in the previous time step and the encoder outputs with the following equation: score_{alignment} = W_{combined} \cdot tanh(W_{decoder} \cdot H_{decoder} + W_{encoder} \cdot H_{encoder}). Definition adapted from here. The RNN will take the hidden state produced in the previous time step and the word embedding of the final output from the previous time step to produce a new hidden state which will be used in the subsequent steps. Similar to Bahdanau Attention, the alignment scores are softmaxed so that the weights will be between 0 to 1. The input to the next decoder step is the concatenation between the generated word from the previous decoder time step (pink) and context vector from the current time step (dark green). instability of trained model). A context vector is an aggregated information of the alignment vectors from the previous step. The challenge of training an effective model can be attributed largely to the lack of training data and training time. By multiplying each encoder hidden state with its softmaxed score (scalar), we obtain the alignment vector [2] or the annotation vector [1]. 1. These weights will affect the encoder hidden states and decoder hidden states, which in turn affect the attention scores. It is often referred to as Multiplicative Attention and was built on top of the Attention mechanism proposed by Bahdanau. This means that for each output that the decoder makes, it has access to the entire input sequence and can selectively pick out specific elements from that sequence to produce the output. In our example, we have 4 encoder hidden states and the current decoder hidden state. 4 However, the more recent adaptations of Attention has seen models move beyond RNNs to Self-Attention and the realm of Transformer models. Just as in Bahdanau Attention, the encoder produces a hidden state for each element in the input sequence. Later we will see in the examples in Sections 2a, 2b and 2c how the architectures make use of the context vector for the decoder. Attention was presented by Dzmitry Bahdanau, et al. Before we look at how attention is used, allow me to share with you the intuition behind a translation task using the seq2seq model. The softmax function will cause the values in the vector to sum up to 1 and each individual value will lie between 0 and 1, therefore representing the weightage each input holds at that time step. Stay tuned! 2015) • Encode each word in the sentence into a vector • When decoding, perform a linear combination of these vectors, weighted by “attention weights” • Use this combination in … These two regularly discuss about every word they read thus far. The idea behind score functions involving the dot product operation (dot product, cosine similarity etc. This is done by masking future positions (setting them to -inf) before the softmax step in the self-attention calculation. Luong attention and Bahdanau attention are two popluar attention mechanisms. """LSTM with attention mechanism: This is an LSTM incorporating an attention mechanism into its hidden states. This is because Attention was originally introduced as a solution to address the main issue surrounding seq2seq models, and to great success. This is exactly the mechanism where alignment takes place. As examples, I will be sharing 4 NMT architectures that were designed in the past 5 years. Unlike in Bahdanau Attention, the decoder in Luong Attention uses the RNN in the first step of the decoding process rather than the last. This will speed up the training process significantly. Here’s how: On the WMT’15 English-to-German, the model achieved a BLEU score of 25.9. Translator A reads the German text while writing down the keywords. The decoder hidden state and encoder outputs will be passed through their individual Linear layer and have their own individual trainable weights. This allows the model to converge faster, although there are some drawbacks involved (e.g. About Gabriel LoyeGabriel is an Artificial Intelligence enthusiast and web developer. al, 2017), [4] Self-Attention GAN (Zhang et. Keep an eye on this space! I first took the whole English and German sentence in input_english_sent and input_german_sent respectively. Since we’ve defined the structure of the Attention encoder-decoder model and understood how it works, let’s see how we can use it for an NLP task - Machine Translation. [paper] Attention-based models describe one particular way in which memory h can be used to derive context vectors c1,c2,…,cU. After generating the alignment scores vector in the previous step, we can then apply a softmax on this vector to obtain the attention weights. These scoring functions make use of the encoder outputs and the decoder hidden state produced in the previous step to calculate the alignment scores. Translator A reads the German text while writing down the keywords. The score functions they experimented were (i). LSTM, GRU) to encode the input sequence. ⁡. al, 2015), [3] Attention Is All You Need (Vaswani et. As the scope of this article is global attention, any references made to “attention” in this article are taken to mean “global attention.”. ( W a [ h i; s j]) where v a and W a are learned attention parameters. Bahdanau's attention is, in fact, a single hidden layer network and thus is able to deal with non-linear relation between the encoder and decoder states. You can try this on a few more examples to test the results of the translator. Again, this step is the same as the one in Bahdanau Attention where the attention weights are multiplied with the encoder outputs. You can connect with Gabriel on LinkedIn and GitHub. So, for a long input text (Fig. Currently, the context vector calculated from the attended vector is fed: into the model's internal states, closely following the model by Xu et al. The paper aimed to improve the sequence-to-sequence model in machine translation by aligning the decoder with the relevant input sentences and implementing Attention. This means that the next word (next output by the decoder) is going to be heavily influenced by this encoder hidden state. During our training cycle, we’ll be using a method called teacher forcing for 50% of the training inputs, which uses the real target outputs as the input to the next step of the decoder instead of our decoder output for the previous time step. In the next sub-sections, let’s examine 3 more seq2seq-based architectures for NMT that implement attention. Later, researchers experimented with Attention Mechanisms for machine translation tasks. The Attention mechanism has revolutionised the way we create NLP models and is currently a standard fixture in most state-of-the-art NLP models. The manner this is done depends on the architecture design. For example, Bahdanau et al., 2015’s Attention … Intuition: GNMT — seq2seq with 8-stacked encoder (+bidirection+residual connections) + attention. For feed-forward neural network score functions, the idea is to let the model learn the alignment weights together with the translation. The following are things to take note about the architecture: The authors achieved a BLEU score of 26.75 on the WMT’14 English-to-French dataset. Answer: Backpropagation, surprise surprise. The implementations of an attention layer can be broken down into 4 steps. in their paper “Neural Machine Translation by Jointly Learning to Align and Translate” that reads as a natural extension of their previous work on the Encoder-Decoder model.Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode each output time step. Definition: alignmentAlignment means matching segments of original text with their corresponding segments of the translation. We will explore these differences in greater detail as we go through the Luong Attention process, which is: As we can already see above, the order of steps in Luong Attention is different from Bahdanau Attention. Note that the junior Translator A has to report to Translator B at every word they read. NLP Datasets: How good is your deep learning model? As the Attention mechanism has undergone multiple adaptations over the years to suit various tasks, there are many different versions of Attention that are applied. In the code implementation of the encoder above, we’re first embedding the input words into word vectors (assuming that it’s a language task) and then passing it through an LSTM. Steps 2 to 4 are repeated until the decoder generates an End Of Sentence token or the output length exceeds a specified maximum length. Attention is an interface between the encoder and decoder that provides the decoder with information from every encoder hidden state (apart from the hidden state in red in Fig. Therefore, the mechanism allows the model to focus and place more “Attention” on the relevant parts of the input sequence as needed. Because most of us must have used Google Translate in one way or another, I feel that it is imperative to talk about Google’s NMT, which was implemented in 2016. However, they didn't become trendy until Google Mind team issued the paper "Recurrent Models of Visual Attention" in 2014. The encoder consists of a stack of 8 LSTMs, where the first is bidirectional (whose outputs are concatenated), and a residual connection exists between outputs from consecutive layers (starting from the 3rd layer). Intuition: How does attention actually work? Take a look, Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau, Effective Approaches to Attention-based Neural Machine Translation (Luong, Sequence to Sequence Learning with Neural Networks (Sutskever, TensorFlow’s seq2seq Tutorial with Attention, Jay Alammar’s Blog on Seq2Seq with Attention, Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (Wu, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers. Dzmitry Bahdanau Chris Pal Recent research has shown that neural text-to-SQL models can effectively translate natural language questions into corresponding SQL queries on unseen databases. The encoder over here is exactly the same as a normal encoder-decoder structure without Attention. This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research. Translator A is the forward RNN, Translator B is the backward RNN. Once done, he starts translating to English word by word. There are multiple designs for attention mechanism. Below are some of the score functions as compiled by Lilian Weng. Fields like Natural Language Processing (NLP) and even Computer Vision have been revolutionized by the attention mechanism This article will be based on the seq2seq framework and how attention can be built on it. The decoder is a GRU whose initial hidden state is a vector modified from the last hidden state from the backward encoder GRU (not shown in the diagram below). Judging by the paper written by Bahdanau ... $\begingroup$ @QtRoS I don't think it was explained there what the keys were, only what values and queries were. Add a description, image, and links to the bahdanau-attention topic page so that developers can more easily learn about it. In this article, I will be covering the main concepts behind Attention, including an implementation of a sequence-to-sequence Attention model, followed by the application of Attention in Transformers and how they can be used for state-of-the-art results. First, he tries to recall, then he shares his answer with Translator B, who improves the answer and shares with Translator C — repeat this until we reach Translator H. Translator H then writes the first translation word, based on the keywords he wrote and the answers he got. This combined vector is then passed through a Linear layer which acts as a classifier for us to obtain the probability scores of the next predicted word. Due to the complex nature of the different languages involved and a large number of vocabulary and grammatical permutations, an effective model will require tons of data and training time before any results can be seen on evaluation data. (Note: the last consolidated encoder hidden state is fed as input to the first time step of the decoder. al, 2015), [2] Effective Approaches to Attention-based Neural Machine Translation (Luong et. et. Dzmitry Bahdanau Jacobs University Bremen, Germany KyungHyun Cho Yoshua Bengio Universite de Montr´ ´eal ABSTRACT Neural machine translation is a recently proposed approach to machine transla-tion. Neural Machine Translation has lately gained a lot of "attention" with the advent of more and more sophisticated but drastically improved models. 0.2), we unreasonably expect the decoder to use just this one vector representation (hoping that it ‘sufficiently summarises the input sequence’) to output a translation. Neural Machine Translation by Jointly Learning to Align and Translate-Bahdanau 2. (2016, Sec. The final output for the time step is obtained by passing the new hidden state through a Linear layer, which acts as a classifier to give the probability scores of the next predicted word. These two attention mechanisms are similar except: 1. ), is to measure the similarity between two vectors. The trouble with seq2seq is that the only information that the decoder receives from the encoder is the last encoder hidden state (the 2 tiny red nodes in Fig. The first type of Attention, commonly referred to as Additive Attention, came from a paper by Dzmitry Bahdanau, which explains the less-descriptive original name. Can I have your Attention please! While translating each German word, he makes use of the keywords he has written down. Most articles on the Attention Mechanism will use the example of sequence-to-sequence (seq2seq) models to explain how it works. For decades, Statistical Machine Translation has been the dominant translation model [9], until the birth of Neural Machine Translation (NMT). I will briefly go through the data preprocessing steps before running through the training procedure. In my next post, I will walk through with you the concept of self-attention and how it has been used in Google’s Transformer and Self-Attention Generative Adversarial Network (SAGAN). Can you translate this paragraph to another language you know, right after this question mark? For completeness, I have also appended their Bilingual Evaluation Understudy (BLEU) scores — a standard metric for evaluating a generated sentence to a reference sentence. Follow me on Twitter @remykarem or LinkedIn. You can run the code implementation in this article on FloydHub using their GPUs on the cloud by clicking the following link and using the main.ipynb notebook. Make learning your daily ritual. The authors use the word ‘align’ in the title of the paper “Neural Machine Translation by Learning to Jointly Align and Translate” to mean adjusting the weights that are directly responsible for the score, while training the model. For these next 3 steps, we will be going through the processes that happen in the Attention Decoder and discuss how the Attention mechanism is utilised. The introduction of the Attention Mechanism in deep learning has improved the success of various models in recent years, and continues to be an omnipresent component in state-of-the-art models. The decoder is a, seq2seq with bidirectional encoder + attention, seq2seq with 2-stacked encoder + attention, GNMT — seq2seq with 8-stacked encoder (+bidirection+residual connections) + attention. Here’s the entire animation: Training and inferenceDuring inference, the input to each decoder time step t is the predicted output from decoder time step t-1. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. The two main differences between Luong Attention and Bahdanau Attention are: There are three types of alignment scoring functions proposed in Luong’s paper compared to Bahdanau’s one type. If we were to test the trained model on sentences it has never seen before, it is unlikely to produce decent results. The goal of this implementation is not to develop a complete English to German translator, but rather just as a sanity check to ensure that our model is able to learn and fit to a set of training data. GNMT is a combination of the previous 2 examples we have seen (heavily inspired by the first [1]). We start by importing the relevant libraries and defining the device we are running our training on (GPU/CPU). Now, let’s understand the mechanism suggested by Bahdanau. Step 3: Multiply each encoder hidden state by its softmaxed score. The standard seq2seq model is generally unable to accurately process long input sequences, since only the last hidden state of the encoder RNN is used as the context vector for the decoder. However, some tasks like translation require more complicated systems. al, 2016), Line-by-Line Word2Vec Implementation (on word embeddings), Step-by-Step Tutorial on Linear Regression with Stochastic Gradient Descent, 10 Gradient Descent Optimisation Algorithms + Cheat Sheet, Counting No. Producing the Encoder Hidden States - Encoder produces hidden states of eachelement in th… How about instead of just one vector representation, let’s give the decoder a vector representation from every encoder time step so that it can make well-informed translations? That’s about it! Hi guys, I’m trying to implement the attention mechanism described in this paper. The entire step-by-step process of applying Attention in Bahdanau’s paper is as follows: 1. Applied an Embedding Layer on both of them. Then, using the softmaxed scores, we aggregate the encoder hidden states using a weighted sum of the encoder hidden states, to get the context vector. See Appendix A for a variety of score functions. NMT is an emerging approach to machine translation that attempts to build and train a single, large neural network that reads an input text and outputs a translation [1]. Likewise, Translator B (who is more senior than Translator A) also reads the same German text, while jotting down the keywords. This implementation of attention is one of the founding attention fathers. He’s always open to learning new things and implementing or researching on novel ideas and technologies. al, 2018), [5] Sequence to Sequence Learning with Neural Networks (Sutskever et. Notice that based on the softmaxed score score^, the distribution of attention is only placed on [5, 0, 1] as expected. Step 5: Feed the context vector into the decoder. But Bahdanau attention take concatenation of forward and backward source hidden state (Top Hidden Layer). , hT i.e 2018 ), [ 3 ] Attention is the backward RNN takes place models to how! Mission is to convert an English sentence to a softmax layer with 8-stacked encoder ( +bidirection+residual connections ) +.! Reading this English text, translator B at every word they read weights will be produced for element. Recurrent models of visual Attention '' in 2014 surrounding seq2seq models, and s the. Cover the more familiar framework is the key innovation behind the recent success of Transformer-based language models as. And implementing Attention for every encoder hidden states relevant input sentences and implementing Attention attributed largely to the model! By masking future positions ( setting them to -inf ) before the softmax step in the mechanism... Using the DyNet framework ( Note: the last step, we 4! A [ h i ; s j ] ) where v a and a! 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Similarity etc a variety of score functions they experimented were ( i ) a score decent results because was... ’ ll gain a clearer picture of how Attention can be found here where. Reach out to me via raimi.bkarim @ gmail.com learn about it a [ h i ; s j )., 2018 ), using a soft Attention model following: Bahdanau et layer and have their own individual weights! Sutskever et by altering the weights in the Self-Attention calculation in their earliest days, Attention for. This paragraph to another language you know, right after this question mark for the encoder is a long! Similarity etc 0.1 ), a vector representation which is like a numerical summary of the... The dot product have been mentioned in this article and training time paragraph to another language you know, after. All these vectors h1, h2, …, hT i.e of all available. 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Achieves 38.95 BLEU on WMT ’ 14 English-to-German input sequence through the data preprocessing before... Of an Attention mechanism: this is done depends on the seq2seq and the word. State/Output will be passed through their individual linear layer and have their own individual trainable weights for,! A long input sentences [ 9 ] summed up to 1 know, right after this question mark dive Python.