Attention and Memory in Deep Learning and NLP – Wild. MLA recent trend in Deep Learning are Attention Mechanisms.
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In an interview, Ilya Sutskever, now the research director of Open. AI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. That sounds exciting. But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are (very) loosely based on the visual attention mechanism found in humans. Human visual attention is well- studied and while there exist different models, all of them essentially come down to being able to focus on a certain region of an image with “high resolution” while perceiving the surrounding image in “low resolution”, and then adjusting the focal point over time.
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Attention in Neural Networks has a long history, particularly in image recognition. Examples include Learning to combine foveal glimpses with a third- order Boltzmann machine or Learning where to Attend with Deep Architectures for Image Tracking. But only recently have attention mechanisms made their way into recurrent neural networks architectures that are typically used in NLP (and increasingly also in vision). That’s what we’ll focus on in this post. What problem does Attention solve? The Legacy Of Arab Islam In Africa Pdf Chart. To understand what attention can do for us, let’s use Neural Machine Translation (NMT) as an example.
Traditional Machine Translation systems typically rely on sophisticated feature engineering based on the statistical properties of text. In short, these systems are complex, and a lot of engineering effort goes into building them. Neural Machine Translation systems work a bit differently.
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In NMT, we map the meaning of a sentence into a fixed- length vector representation and then generate a translation based on that vector. By not relying on things like n- gram counts and instead trying to capture the higher- level meaning of a text, NMT systems generalize to new sentences better than many other approaches. Perhaps more importantly, NTM systems are much easier to build and train, and they don’t require any manual feature engineering.
In fact, a simple implementation in Tensorflow is no more than a few hundred lines of code. Most NMT systems work by encoding the source sentence (e. The decoder keeps generating words until a special end of sentence token is produced. Here, the vectors represent the internal state of the encoder. If you look closely, you can see that the decoder is supposed to generate a translation solely based on the last hidden state ( above) from the encoder.
This vector must encode everything we need to know about the source sentence. It must fully capture its meaning. In more technical terms, that vector is a sentence embedding. In fact, if you plot the embeddings of different sentences in a low dimensional space using PCA or t- SNE for dimensionality reduction, you can see that semantically similar phrases end up close to each other. That’s pretty amazing.
Still, it seems somewhat unreasonable to assume that we can encode all information about a potentially very long sentence into a single vector and then have the decoder produce a good translation based on only that. Let’s say your source sentence is 5. The first word of the English translation is probably highly correlated with the first word of the source sentence. But that means decoder has to consider information from 5. Recurrent Neural Networks are known to have problems dealing with such long- range dependencies. In theory, architectures like LSTMs should be able to deal with this, but in practice long- range dependencies are still problematic.
For example, researchers have found that reversing the source sequence (feeding it backwards into the encoder) produces significantly better results because it shortens the path from the decoder to the relevant parts of the encoder. Similarly, feeding an input sequence twice also seems to help a network to better memorize things. I consider the approach of reversing a sentence a “hack”. It makes things work better in practice, but it’s not a principled solution.
Most translation benchmarks are done on languages like French and German, which are quite similar to English (even Chinese word order is quite similar to English). But there are languages (like Japanese) where the last word of a sentence could be highly predictive of the first word in an English translation. In that case, reversing the input would make things worse.
So, what’s an alternative? Attention Mechanisms.
With an attention mechanism we no longer try encode the full source sentence into a fixed- length vector. Rather, we allow the decoder to “attend” to different parts of the source sentence at each step of the output generation. Importantly, we let the model learn what to attend to based on the input sentence and what it has produced so far.
So, in languages that are pretty well aligned (like English and German) the decoder would probably choose to attend to things sequentially. Attending to the first word when producing the first English word, and so on. That’s what was done in Neural Machine Translation by Jointly Learning to Align and Translate and look as follows: Here, The . The above illustration uses a bidirectional recurrent network, but that’s not important and you can just ignore the inverse direction. The important part is that each decoder output word now depends on a weighted combination of all the input states, not just the last state.
So, if is a large number, this would mean that the decoder pays a lot of attention to the second state in the source sentence while producing the third word of the target sentence. The are typically normalized to sum to 1 (so they are a distribution over the input states). A big advantage of attention is that it gives us the ability to interpret and visualize what the model is doing. For example, by visualizing the attention weight matrix when a sentence is translated, we can understand how the model is translating: Here we see that while translating from French to English, the network attends sequentially to each input state, but sometimes it attends to two words at time while producing an output, as in translation “la Syrie” to “Syria” for example. The Cost of Attention.
If we look a bit more look closely at the equation for attention we can see that attention comes at a cost. We need to calculate an attention value for each combination of input and output word. If you have a 5. 0- word input sequence and generate a 5.
That’s not too bad, but if you do character- level computations and deal with sequences consisting of hundreds of tokens the above attention mechanisms can become prohibitively expensive. Actually, that’s quite counterintuitive.
Human attention is something that’s supposed to save computational resources. By focusing on one thing, we can neglect many other things. But that’s not really what we’re doing in the above model. We’re essentially looking at everything in detail before deciding what to focus on. Intuitively that’s equivalent outputting a translated word, and then going back through all of your internal memory of the text in order to decide which word to produce next. That seems like a waste, and not at all what humans are doing.
In fact, it’s more akin to memory access, not attention, which in my opinion is somewhat of a misnomer (more on that below). Still, that hasn’t stopped attention mechanisms from becoming quite popular and performing well on many tasks. An alternative approach to attention is to use Reinforcement Learning to predict an approximate location to focus to. That sounds a lot more like human attention, and that’s what’s done in Recurrent Models of Visual Attention. Attention beyond Machine Translation. So far we’ve looked at attention applied to Machine Translation. But the same attention mechanism from above can be applied to any recurrent model.
So let’s look at a few more examples. In Show, Attend and Tell the authors apply attention mechanisms to the problem of generating image descriptions.