Understanding Transformers Part 3: Combining Meaning and Position
This article explains how transformers combine word embeddings with positional encoding to capture both semantic meaning and word order information.
Why it matters
This combination of semantics and position is a core aspect of how transformers work, allowing them to understand language at a deeper level than previous models.
Key Points
- 1Positional encoding assigns unique sequences of values to each word based on their position in the sentence
- 2These positional values are added to the word embeddings to create final representations that encode both meaning and position
- 3Changing the word order results in different final representations, even with the same words, allowing transformers to understand sequence
- 4This combination of semantics and position is a key aspect of how transformers work
Details
The article builds on the previous installment, which covered how positional encoding is generated using sine and cosine waves. It then explains the process of applying these positional values to each word in a sentence. By doing this, each word gets its own unique sequence of positional information. These positional values are then added to the word embeddings, which capture the semantic meaning of the words. The final representations now contain both the original meaning of the words as well as their relative positions in the sequence. This allows transformers to understand not just the individual words, but also their order and relationship to each other. Even if the same words are used in a different order, the final representations will be different, enabling transformers to model sequence and context effectively.
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