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Byte pair encoding [1] [2] (also known as BPE, or digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. [4]
High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:
Convolutional code with any code rate can be designed based on polynomial selection; [15] however, in practice, a puncturing procedure is often used to achieve the required code rate. Puncturing is a technique used to make a m/n rate code from a "basic" low-rate (e.g., 1/n) code. It is achieved by deleting of some bits in the encoder output.
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Upper case variables represent the entire sentence, and not just the current word. For example, H is a matrix of the encoder hidden state—one word per column. S, T: S, decoder hidden state; T, target word embedding. In the Pytorch Tutorial variant training phase, T alternates between 2 sources depending on the level of teacher forcing used. T ...
This paper's goal was to improve upon 2014 seq2seq technology, [10] and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. [11] The following year in 2018, BERT was introduced and quickly became "ubiquitous". [12] Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Code-excited linear prediction (CELP) is a linear predictive speech coding algorithm originally proposed by Manfred R. Schroeder and Bishnu S. Atal in 1985. At the time, it provided significantly better quality than existing low bit-rate algorithms, such as residual-excited linear prediction (RELP) and linear predictive coding (LPC) vocoders (e.g., FS-1015).