Transformers and the Power of Positional Encoding [Transformers Series]
How Transformers Find Order In Data
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Not sure where to begin? You can read the last post in the Transformers Series below:
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The Rise of The Transformer
Before delving into positional encoding, let's briefly touch upon the rise of Transformers. In 2017, the introduction of the Transformer architecture in the seminal paper “Attention is All You Need” revolutionized the field of natural language processing (NLP) and eventually the whole of machine learning.
Unlike traditional sequential models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), Transformers leveraged the power of attention mechanisms to capture long-range dependencies efficiently. This breakthrough paved the way for Transformer-based models like BERT, GPT, and T5, which achieved remarkable success across NLP tasks.
Introduction
At the heart of the Transformer architecture lies the self-attention mechanism, enabling the model to weigh the relevance of different tokens in the input sequence dynamically.
However, unlike sequential models such as RNNs that inherently possess positional information through the order of tokens, Transformers treat input sequences as unordered sets. This lack of positional understanding could pose a significant challenge, particularly for tasks where the order of elements is crucial, such as language understanding.
This is where positional encoding comes into play. Positional encoding is a mechanism used to inject positional information into the input embeddings, enabling the Transformer to discern the sequential order of tokens. By encoding each token's position within the sequence, positional encoding equips the model with the necessary spatial awareness to process sequences effectively.
In this article, we discuss the importance of positional encoding in Transformers.
