23.11.2020 · Positional Encoding Unlike sequential algorithms like `RNN`s and `LSTM`, transformers don’t have a mechanism built in to capture the relative positions of words in a sentence. This is important...
13.05.2021 · Positional embeddings are there to give a transformer knowledge about the position of the input vectors. They are added (not concatenated) to corresponding input vectors. Encoding depends on three values: pos — position of the vector i — index within the vector d_ {model} — dimension of the input
Positional encoding is a re-representation of the values of a word and its position in a sentence (given that is not the same to be at the beginning that at the end or middle).
transformer’s sinusoidal positional encodings, allowing us to instead use a novel positional encoding scheme to represent node positions within trees. We evalu-ated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over both sequence-to-sequence
Linear Relationships in the Transformer’s Positional Encoding In June 2017, Vaswani et al. published the paper “Attention Is All You Need” describing the “Transformer” architecture, which is a purely attention based sequence to sequence model. It can be applied to many tasks, such as language translation and text summarization.
What a positional encoder does is to get help of the cyclic nature of sin(x) and cos(x) functions to return information of the position of a word in a sentence.
A positional encoding is a finite dimensional representation of the location or “position” of items in a sequence. Given some sequence A = [a_0, …, a_{n-1}], ...