Introduction to Transformer positional encoding and how to improve it

Transformer is a deep learning model widely used in natural language processing tasks. It uses a self-attention mechanism to capture the relationship between words in the sequence, but ignores the position order of words in the sequence, which may lead to information loss. To solve this problem, Transformer introduces positional encoding. The basic principle of positional encoding is to assign each word in the sequence a position vector, which contains information about the position of the word in the sequence. This way, the model can take into account the positional information of the word by adding the position vector to the word embedding vector. A common method of position encoding is to use sine and cosine functions to generate position vectors. Specifically, for each position and each dimension, the value of the position vector is composed of a sine function and a cosine function. This encoding method allows the model to learn the relationship between different locations. In addition to traditional position encoding methods, some improved methods have been proposed. For example, one can use learned position encoding, where position vectors are learned through a neural network. This method can adaptively adjust the position vector during the training process to better capture the position information in the sequence. In short, the Transformer model uses positional encoding to consider the order of words

1. Basic principles

In Transformer, positional encoding is to convert position information Encoded into a vector. It is added with the word’s embedding vector to get the final representation of each word. The specific calculation method is as follows:

PE_{(i,2j)}=sin(\frac{i}{10000^{2j/d_{model}}})

PE_{(i,2j 1)}=cos(\frac{i}{10000^{2j/d_{model}}})

Among them, i is the position of the word, j is the dimension of the position encoding vector, and d_{model} is the dimension of the Transformer model. With this formula, we can calculate the position encoding value for each position and each dimension. We can combine these values ​​into a positional encoding matrix and then add it to the word embedding matrix to obtain a positional encoding representation of each word.

2. Improvement methods

Although Transformer's positional encoding performs well in many tasks, there are still some improvements that can be used.

1. Learning positional encoding

In the traditional Transformer model, positional encoding is calculated based on a fixed formula, which cannot adapt to different tasks and specific needs of different data sets. Therefore, researchers have proposed some methods to learn positional encoding. One approach is to use neural networks to learn positional encodings. Specifically, researchers use autoencoders or convolutional neural networks to learn positional encoding so that the positional encoding can be adapted to the specific needs of the task and data set. The advantage of this method is that the position encoding can be adaptively adjusted, thereby improving the generalization ability of the model.

2. Random position encoding

Another improvement method is to use random position encoding. This method replaces the fixed position encoding formula by randomly sampling a set of position encoding vectors. The advantage of this method is that it can increase the diversity of the model, thereby improving the robustness and generalization ability of the model. However, since the random position encoding is randomly generated at each training time, more training time is required.

3. Multi-scale position encoding

Multi-scale position encoding is a method to improve position by combining multiple position encoding matrices Coding method. Specifically, the researchers added position encoding matrices at different scales to obtain a richer position encoding representation. The advantage of this method is that it can capture position information at different scales, thereby improving the performance of the model.

4. Local positional encoding

Local positional encoding is a method to improve positional encoding by limiting positional encoding to a local area. Specifically, the researchers limited the calculation of positional encoding to a certain range around the current word, thereby reducing the complexity of positional encoding. The advantage of this approach is that it can reduce computational costs while also improving model performance.

In short, Transformer positional encoding is an important technique that can help the model capture the positional information between words in the sequence, thereby improving the performance of the model. Although traditional positional encoding performs well in many tasks, there are some improvements that can be used. These improvement methods can be selected and combined according to the needs of the task and data set, thereby improving the performance of the model.

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