From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
author：Refrigeration plant
From Seq2Seq to Attention: Revolutionizing Sequence Modeling
The attention mechanism is an important tool in neural machine translation models to solve the problems of context compression, short-term memory limitation, and bias, and its origins can be traced back a long time. This article introduces the basic principles of the mechanism of attention and explains in detail the principles of additive attention and Bahdanau attention. The three main components of the attention mechanism are the encoder, the decoder, and the attention scoring function. The encoder and decoder consist of bidirectional and one-way RNNs, and through the attention scoring function, the network can automatically (softly) search the parts of the source sentence that are related to the predicted target word, resulting in more accurate and context-aware sequences.
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
From Seq2Seq to Attention: Revolutionizing the Attention Mechanism of Sequence Modeling is a solution to the problem of context compression, short-term memory limitations, and bias in neural machine translation models
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