NLP Highlights

36 - Attention Is All You Need, with Ashish Vaswani and Jakob Uszkoreit

Oct 23, 2017

Ashish Vaswani and Jakob Uszkoreit, co-authors of the "Attention Is All You Need" paper, discuss the motivation behind replacing RNNs and CNNs with a self-attention mechanism in the Transformer model. They delve into topics such as the positional encoding mechanism, multi-headed attention, replacing encoders in other models, and what self-attention actually learns. They highlight how lower layers learn n-grams and higher layers learn coreference, showcasing the power of the self-attention mechanism.

41:15

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Episode guests

Ashish Vaswani

Jakob Uszkoreit

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Quick takeaways

The proposed attention network in the podcast paper addresses the limitations of RNNs and CNNs by providing a more global view within each layer, allowing for more efficient parallelization.

The self-attention mechanism in the paper allows for comparing each pair of positions in a sequence, generating attention distributions, and aggregating representations for other positions. It offers improved efficiency, lower computational complexity, and demonstrated patterns linked to coreference resolution and lexical disambiguation.

Deep dives

The limitations of RNNs and CNNs

The paper proposes a new type of attention network to overcome the limitations of RNNs and CNNs in modeling sequences. RNNs struggle to learn long-range dependencies, while CNNs need multiple layers to obtain a global view due to their limited receptive field size. The proposed attention network addresses these issues by providing a more global view within each layer and allowing for more efficient parallelization.

Introduction

6min

Self-Attention Mechanism for Parallelization and Dependency Connections

18min

Exploring the Working of a Model and the Importance of Sinusoids in Embeddings

2min

Discussion on Hypotheses, Substitutions, and Dependencies in the Learning Process

2min

Exploring a New Encoder for Various Tasks

3min

Using Attention Mechanisms in Natural Language Processing

10min

NIPS 2017 paper. We dig into the details of the Transformer, from the "attention is all you need" paper. Ashish and Jakob give us some motivation for replacing RNNs and CNNs with a more parallelizable self-attention mechanism, they describe how this mechanism works, and then we spend the bulk of the episode trying to get their intuitions for _why_ it works. We discuss the positional encoding mechanism, multi-headed attention, trying to use these ideas to replace encoders in other models, and what the self-attention actually learns. Turns out that the lower layers learn something like n-grams (similar to CNNs), and the higher layers learn more semantic-y things, like coreference. https://www.semanticscholar.org/paper/Attention-Is-All-You-Need-Vaswani-Shazeer/0737da0767d77606169cbf4187b83e1ab62f6077 Minor correction: Talking about complexity equations without the paper in front of you can be tricky, and Ashish and Jakob may have gotten some of the details slightly wrong when we were discussing computational complexity. The high-level point is that self-attention is cheaper than RNNs when the hidden dimension is higher than the sequence length. See the paper for more details.

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NLP Highlights

36 - Attention Is All You Need, with Ashish Vaswani and Jakob Uszkoreit

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The limitations of RNNs and CNNs

The key idea of the paper

Results and applications of the proposed model

Insights about attention distributions

Remember Everything You Learn from Podcasts