Positional Encoding Generator: An Overview
If you have ever encountered natural language processing or machine translation, then you may have come across the term positional encoding. A positional encoding is a mechanism that helps a neural network understand the order and sequence of tokens in a sequence. It does this by encoding each token with a unique set of numbers that represent its position in the sequence. This way, the neural network can differentiate each token based on its context or its relationship to other tokens in the sequence. One way to generate positional encodings is by using a module called the Positional Encoding Generator or PEG.
What is PEG?
PEG is a module used in Conditional Position Encoding (CPE) position embeddings. It produces positional encodings that are conditioned on the local neighborhood of an input token. What this means is that it generates positional encodings that are specific to each token's position relative to its neighboring tokens. PEG is a dynamic module, which means that the positional encodings it generates are computed based on the specific context of the input sequence.
PEG works by first reshaping the input sequence to a 2D image space. Then, it applies a function denoted as "F" to each local patch in the image to produce the conditional positional encodings. Finally, these positional encodings are concatenated with the input sequence and sent to another neural network layer for processing.
How does PEG work?
Let's say we have an input sequence of dimension $X\in\mathbb{R}^{B \times N \times C}$, where B is the batch size, N is the sequence length, and C is the number of features in each token. First, we reshape the flattened input sequence $X$ back to $X^{'}\in\mathbb{R}^{B \times H \times W \times C}$ in the 2D image space, where H and W are the height and width of the image, respectively.
Next, we apply a function denoted as "F" to each local patch in the image to produce the conditional positional encodings. The function "F" can be implemented in various forms, such as separable convolutions and many others. One way to implement "F" is through a 2D convolution with kernel k (where k≥3) and (k-1)/2 zero paddings. The zero paddings here are important to make the model aware of the absolute positions of each token.
Finally, the generated conditional positional encodings are concatenated with the input sequence, and this concatenated sequence is sent to another neural network layer for further processing. By conditioning on the local neighborhood of the input tokens, PEG helps the neural network better understand the relative positions of tokens in the input sequence.
Why is PEG important?
Conditional Position Encoding (CPE) using PEG has shown to be an effective technique for improving the performance of neural machine translation models. By using PEG, neural networks can better capture the local context and position of each token in a sequence, leading to better translation accuracy. Additionally, because PEG is dynamically generated based on the local neighborhood of each input token, it can adapt to different input sequences, making it a versatile tool for natural language processing tasks.
Positional Encoding Generator (PEG) is a module that helps generate positional encodings for a sequence of tokens. It works by conditioning on the local neighborhood of each input token, and generating conditional positional encodings that are specific to each token's position relative to its neighbors. PEG can be used in various natural language processing tasks, such as machine translation, and has been shown to improve the accuracy of neural network models. Overall, PEG is an important tool for helping neural networks better understand the order and sequence of tokens in a sequence.
