(Note: the last consolidated encoder hidden state is fed as input to the first time step of the decoder. In our example, we have 4 encoder hidden states and the current decoder hidden state. The implementations of an attention layer can be broken down into 4 steps.įirst, prepare all the available encoder hidden states (green) and the first decoder hidden state (red). Then, using the softmax scores, we aggregate the encoder hidden states using a weighted sum of the encoder hidden states to get the context vector. While translating each English word, it makes use of the keywords it has understood.Īttention places different focus on different words by assigning each word with a score. Let's take an example where a translator reads the English(input language) sentence while writing down the keywords from the start till the end, after which it starts translating to Portuguese (the output language). The attention weights are calculated by normalizing the output score of a feed-forward neural network described by the function that captures the alignment between input at j and output at i. The context vector ci for the output word yi is generated using the weighted sum of the annotations: The attention model computes a set of attention weights denoted by α(t,1), α(t,2).,α(t,t) because not all the inputs would be used in generating the corresponding output. Similarly, we create context vectors from different sets of words with different α values. Also, we have α1, α2, and α3 as weights, and the sum of all weights within one window is equal to 1. Similarly, we’ll create a context vector c2 using these four words. Using these four words, we’ll create a context vector c1, which is given as input to the decoder. Here, we give attention to some words by considering a window size Tx (say four words x1, x2, x3, and x4). The idea is to keep the decoder as it is, and we just replace sequential RNN/LSTM with bidirectional RNN/LSTM in the encoder. Source: Feed the context vector to decoder. The below figure demonstrates an Encoder-Decoder architecture with an attention layer. This helps the model to cope efficiently with long input sentences. With this framework, the model is able to selectively focus on valuable parts of the input sequence and hence, learn the association between them. In simpler words, it only pays attention to some input words.Īttention is an interface connecting the encoder and decoder that provides the decoder with information from every encoder hidden state. So coming back, the core idea is each time the model predicts an output word, it only uses parts of the input where the most relevant information is concentrated instead of the entire sequence. This very paper laid the foundation of the famous paper Attention is All You Need by Vaswani et al., on transformers that revolutionized the deep learning arena with the concept of parallel processing of words instead of processing them sequentially. in their 2014 paper “ Neural Machine Translation by Jointly Learning to Align and Translate,” which reads as a natural extension of their previous work on the Encoder-Decoder model. Hence, what’s reflected from the graph above is that the encoder-decoder unit works well for shorter sentences (high bleu score).Īttention was presented by Dzmitry Bahdanau, et al. The above graph shows that the encoder-decoder unit fails to memorize the whole long sentence. The attention mechanism was created to resolve this problem of long dependencies.īLEU (Bilingual Evaluation Understudy) is a score for comparing a candidate translation of text to one or more reference translations. Often it has forgotten the earlier elements of the input sequence once it has processed the complete sequence. The decoder is then initialized with this context vector, using which it starts producing the transformed or translated output.Ī critical disadvantage of this fixed-length context vector design is the inability of the system to retain longer sequences. This representation is anticipated to be a good summary of the complete input sequence. The seq2seq model is normally composed of an encoder-decoder architecture, where the encoder processes the input sequence and encodes/compresses the information into a context vector (or “thought vector”) of fixed length. Even though this mechanism is now used in various problems like image captioning and others, it was originally designed in the context of Neural Machine Translation using Seq2Seq Models. Attention is one of the most prominent ideas in the Deep Learning community.
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