#Attention attention please full

This distinction has led to broader categories like Global/Soft and Local/Hard attention. There exist multiple alignment scores, the most popular are additive (also known as concat, Bahdanau et al 2015), location-base, general and dot-product ( Luong 2015). The key question is, how does the model know where to focus? It calculates a score known as alignment score which quantifies how much attention we should give to each input. While in English, adjectives are used before the noun, in French they go after, so in this case the model is shifting the attention to the words that appear afterwards. We can see the power of the attention mechanism when the model translates ‘European Economic Area’ to ‘zone economique européenne’. Each pixel shows where the model is shifting its attention, the whiter the area the more attention it was paying, the darker the less (like the previous example).įor example, to generate the word ‘accord’ it mainly focuses on ‘agreement’. The x-axis corresponds to the source sentence in English and the y-axis to the generated translation in French.

#Attention attention please full

For each word the decoder wants to translate, it does not need the full matrix because not all the words provide relevant information, so how can the decoder know which part of the matrix to focus on or to pay more attention to?įigure 2: Image from ‘Neural Machine Translation by jointly learning to align and translate’ This solve the problem of not having enough information with just one vector, but it also adds the exact opposite problem, too much information. With this new structure we are keeping all the hidden states for every time step.Īs we can see in the image below while previously the output of the encoder was one vector, we now have a matrix composed by each of the hidden states. In the previous structure we were just passing the hidden state from the last time step. There exist two major differences which we will analyse in the following sections 2- St ack of hidden states

Consequently, its performance decreases with long sentences as it tends to forget parts of it, the hidden vector becomes a bottleneck.Īttention mechanism is built upon the encoder decoder structure we have just analysed. As we have said all the input sentence meaning is captured in one vector, so as the length of the sentence increase, the more difficult it gets for the model to capture the information in this vector. This architecture has demonstrated its great capacities in Seq2Seq problems, however it also has one important limitation. Image by the author: Encoding process, the output is hidden state of the last time step. In the image below we can see how the model looks like for a translation example: This architecture is so powerful that even Google has adopted it as the core technology for Google Translate. Models with different sequences lengths are, for example, sentiment analysis that receives a sequence of words and outputs a number, or Image captioning models where the input is an image and the output is a sequence of words. Its main benefit is that we can detach the encoder and the decoder, so they have different lengths. Encoder Decoder, the bottleneck problemġ - Encoder Decoder, the bottleneck problemĪn encoder decoder architecture is built with RNN and it is widely used in neural machine translation (NMT) and sequence to sequence (Seq2Seq) prediction.

We will be looking at the following sections: It was first performed by Miracle☆Kiratts in episode 32.Throughout this article we will first understand the limits of the techniques we were using to then introduce the new mechanisms to overcome the problems they were facing. Maiden Attention Please ( 乙女アテンションプリーズ, Maiden Attention Please) (officially Otome Attention Please) is a group song that appears in Kiratto Pri☆Chan.

Episode 32 - I Tried Pursuing a Worrying Rumor!