Transformer-based Deep Neural Network (DNN) Model

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A Transformer-based Deep Neural Network (DNN) Model is an sequence-to-* neural network composed of transformer blocks.

References

2024

2021

  • (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Transformer_(machine_learning_model) Retrieved:2021-5-24.
    • A transformer is a deep learning model that adopts the mechanism of attention, weighing the influence of different parts of the input data. It is used primarily in the field of natural language processing (NLP). It also has applications in tasks such as video understanding. [1] Like recurrent neural networks (RNNs), transformers are designed to handle sequential input data, such as natural language, for tasks such as translation and text summarization. However, unlike RNNs, transformers do not require that the sequential data be processed in order. Rather, the attention operation provides context for any position in the input sequence. For example, if the input data is a natural language sentence, the transformer does not need to process the beginning of the sentence before the end. Rather, it identifies the context that confers meaning to a word in the sentence. Due to this feature, the transformer allows for much more parallelization than RNNs and therefore reduces training times. Transformers have rapidly become the model of choice for NLP problems, replacing older RNN models such as long short-term memory (LSTM). Since the transformer model facilitates more parallelization during training, it has enabled training on larger datasets than was once possible. This led to the development of pretrained systems such as BERT (Bidirectional Encoder Representations from transformers) and GPT (Generative Pre-trained transformer), which have been trained with large language datasets, such as Wikipedia Corpus and Common Crawl, and can be fine-tuned to specific tasks.

2020

  1. ↑ Is Space-Time Attention All You Need for Video Understanding?[1] Bertasias, Wang, and Torresani

2020

2019b

  • (Horev, 2019) ⇒ Rani Horev (Jan, 2017). "Transformer-XL Explained: Combining Transformers and RNNs into a State-of-the-art Language Model". In: Medium -Towards Data Science Blog.
    • QUOTE: ... Transformers, invented in 2017, introduced a new approach  —  attention modules. Instead of processing tokens one by one, attention modules receive a segment of tokens and learn the dependencies between all of them at once using three learned weight matrices  —  Query, Key and Value  —  that form an Attention Head. The Transformer network consists of multiple layers, each with several Attention Heads (and additional layers), used to learn different relationships between tokens.

      As in many NLP models, the input tokens are first embedded into vectors. Due to the concurrent processing in the attention module, the model also needs to add information about the order of the tokens, a step named Positional Encoding, that helps the network learn their position. In general, this step is done with a sinusoidal function that generates a vector according to the token’s position, without any learned parameters.


      An example of a single Attention Head on a single token (E1).
      Its output is calculated using its Query vector, and the Key and Value vectors of all tokens (In the chart we show only one additional token E2) 
      —  The Query and the Key define the weight of each token, and the output is the weighted sum of all Value vectors.

2018a

  • (Gouws, 2018) ⇒ Stephan Gouws (August, 2018) ⇒ "Moving Beyond Translation with the Universal Transformer". In: Google AI Blog
    • QUOTE: ... Last year we released the Transformer, a new machine learning model that showed remarkable success over existing algorithms for machine translation and other language understanding tasks. Before the Transformer, most neural network based approaches to machine translation relied on recurrent neural networks (RNNs) which operate sequentially (e.g. translating words in a sentence one-after-the-other) using recurrence (i.e. the output of each step feeds into the next). While RNNs are very powerful at modeling sequences, their sequential nature means that they are quite slow to train, as longer sentences need more processing steps, and their recurrent structure also makes them notoriously difficult to train properly. In contrast to RNN-based approaches, the Transformer used no recurrence, instead processing all words or symbols in the sequence in parallel while making use of a self-attention mechanism to incorporate context from words farther away. By processing all words in parallel and letting each word attend to other words in the sentence over multiple processing steps, the Transformer was much faster to train than recurrent models. Remarkably, it also yielded much better translation results than RNNs. However, on smaller and more structured language understanding tasks, or even simple algorithmic tasks such as copying a string (e.g. to transform an input of “abc” to “abcabc”), the Transformer does not perform very well. In contrast, models that perform well on these tasks, like the Neural GPU and Neural Turing Machine, fail on large-scale language understanding tasks like translation. In “Universal Transformers” we extend the standard Transformer to be computationally universal (Turing complete) using a novel, efficient flavor of parallel-in-time recurrence which yields stronger results across a wider range of tasks. We built on the parallel structure of the Transformer to retain its fast training speed, but we replaced the Transformer’s fixed stack of different transformation functions with several applications of a single, parallel-in-time recurrent transformation function (i.e. the same learned transformation function is applied to all symbols in parallel over multiple processing steps, where the output of each step feeds into the next). Crucially, where an RNN processes a sequence symbol-by-symbol (left to right), the Universal Transformer processes all symbols at the same time (like the Transformer), but then refines its interpretation of every symbol in parallel over a variable number of recurrent processing steps using self-attention. This parallel-in-time recurrence mechanism is both faster than the serial recurrence used in RNNs, and also makes the Universal Transformer more powerful than the standard feedforward Transformer. ...

2018b

2018c

2018d

2018e

  • (Alammar,2018) ⇒ Jay Alammar. (2018). “The Illustrated Transformer."
    • QUOTE: ... In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. ... The biggest benefit, however, comes from how The Transformer lends itself to parallelization. ...


      The encoder’s inputs first flow through a self-attention layer – a layer that helps the encoder look at other words in the input sentence as it encodes a specific word. We’ll look closer at self-attention later in the post. The outputs of the self-attention layer are fed to a feed-forward neural network. The exact same feed-forward network is independently applied to each position. The decoder has both those layers, but between them is an attention layer that helps the decoder focus on relevant parts of the input sentence (similar what attention does in seq2seq models).

2017b

  • (Uszkoreit, 2019) ⇒ Jakob Uszkoreit (August, 2017). "Transformer: A Novel Neural Network Architecture for Language Understanding". In: Google AI Blog.
    • QUOTE: ... In contrast, the Transformer only performs a small, constant number of steps (chosen empirically). In each step, it applies a self-attention mechanism which directly models relationships between all words in a sentence, regardless of their respective position. In the earlier example “I arrived at the bank after crossing the river”, to determine that the word “bank” refers to the shore of a river and not a financial institution, the Transformer can learn to immediately attend to the word “river” and make this decision in a single step. In fact, in our English-French translation model we observe exactly this behavior. More specifically, to compute the next representation for a given word - “bank” for example - the Transformer compares it to every other word in the sentence. The result of these comparisons is an attention score for every other word in the sentence. These attention scores determine how much each of the other words should contribute to the next representation of “bank”. In the example, the disambiguating “river” could receive a high attention score when computing a new representation for “bank”. The attention scores are then used as weights for a weighted average of all words’ representations which is fed into a fully-connected network to generate a new representation for “bank”, reflecting that the sentence is talking about a river bank. The animation below illustrates how we apply the Transformer to machine translation. Neural networks for machine translation typically contain an encoder reading the input sentence and generating a representation of it. A decoder then generates the output sentence word by word while consulting the representation generated by the encoder. The Transformer starts by generating initial representations, or embeddings, for each word. These are represented by the unfilled circles. Then, using self-attention, it aggregates information from all of the other words, generating a new representation per word informed by the entire context, represented by the filled balls. This step is then repeated multiple times in parallel for all words, successively generating new representations.

      The decoder operates similarly, but generates one word at a time, from left to right. It attends not only to the other previously generated words, but also to the final representations generated by the encoder.

2017a

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