Stanford XCS224U: NLU I Contextual Word Representations, Part 3: Positional Encoding I Spring 2023 | Summary and Q&A

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August 17, 2023
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Stanford XCS224U: NLU I Contextual Word Representations, Part 3: Positional Encoding I Spring 2023

TL;DR

Positional encoding is crucial in Transformer-based models to retain word order and enable hierarchical representation, and relative positional encoding is the most promising scheme.

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Key Insights

  • 👣 Transformers have limited capacity to track word order, making positional encoding crucial for retaining the distinction between different input sequences.
  • 🧘 Absolute positional encoding suffers from the need to decide on the set of positions ahead of time, hindering generalization to new positions.
  • ⚾ Frequency-based positional encoding overcomes the first limitation but still struggles with positional dependence.
  • 👻 Relative positional encoding, with a positional encoding window, provides a promising solution by allowing for generalization and relative position tracking.
  • 🧘 Relative positional encoding is embedded in the attention layer of the Transformer, introducing position relativity without relying heavily on positional embeddings.
  • 🧘 Relative positional encoding does not impose limitations on sequence length, as it focuses on relative position rather than absolute position.
  • 💯 The core vectors involved in relative positional encoding, such as 0, 1, and -1, remain constant for phrases like "the rock" regardless of their position in the input sequence.

Transcript

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Questions & Answers

Q: What is the role of positional encoding in the Transformer model?

Positional encoding is important in the Transformer model to retain word order, as the attention mechanisms are not directional. It also allows for hierarchical representation, such as in natural language inference tasks.

Q: How does absolute positional encoding hinder generalization?

Absolute positional encoding requires the set of positions to be decided ahead of time, limiting the model's ability to handle new positions. It also adds positional representations equally to word representations, making it challenging for the model to distinguish between the same phrase appearing in different positions.

Q: What is frequency-based positional encoding?

Frequency-based positional encoding uses sine and cosine functions to encode positional information. Higher positions oscillate more frequently, and these frequencies are encoded in the positional vectors. However, it still suffers from the limitations of absolute positional encoding.

Q: What is relative positional encoding?

Relative positional encoding introduces a positional encoding window, where a small number of positional vectors represent relative positions. It allows for generalization to new positions and overcomes the limitations of absolute and frequency-based positional encoding.

Summary & Key Takeaways

  • Positional encoding is essential in Transformers as the attention mechanisms are not directional and have limited capacity to track word order.

  • Absolute positional encoding, where word and positional representations are added together, can hinder generalization to new positions.

  • Frequency-based positional encoding, using sine and cosine functions, allows for the representation of any position but still suffers from positional dependence.

  • Relative positional encoding, with a positional encoding window, overcomes the limitations of the previous schemes and enables better generalization to new positions.

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