Pointer Network and Sequence-to-Sequence in Machine Learning

Delve into the fascinating world of Pointer Network and Sequence-to-Sequence models in machine learning. Explore the interconnected concepts of encoder-decoder architecture, attention mechanisms, and the application of these models in tasks like summarization, machine translation, and chat-bot development. Witness how the process flow guides the output generation based on attention weights, ending when the 'END' token receives the highest attention. Uncover the potential of these models in various real-world applications.

• Machine Learning
• Encoder-Decoder
• Sequence-to-Sequence
• Attention Mechanisms

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Uploaded on Mar 03, 2025 | 0 Views

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Presentation Transcript

1. Pointer Network

2. Pointer Network 5 3 4 2 7 6 NN ?1 ?1 ?2 ?2 ?3 ?3 ?4 ?4 coordinate of P1

3. Sequence-to-sequence? machine ~ learning ~ Encoder Decoder

4. Problem? Sequence-to-sequence? 1 4 2 {1, 2, 3, 4, END} ~ ~ ~ Of course, one can add attention. ?2 ?2 ?3 ?3 ?4 ?4 ?1 ?1 Encoder Decoder

5. ?0 ?0: END Pointer Network Attention Weight key ?0 0.5 0 1 2 3 4 ?2 ?2 ?3 ?3 ?4 ?4 ?1 ?1 ?0 ?0

6. ?0 ?0: END Pointer Network Output: 1 What decoder can output depends on the input. ~ argmax from this distribution key ?0 ?1 0.0 0.5 0.3 0.2 0.0 ?1 ?1 0 1 2 3 4 ?2 ?2 ?3 ?3 ?4 ?4 ?1 ?1 ?0 ?0

7. ?0 ?0: END Pointer Network Output: 4 What decoder can output depends on the input. ~ argmax from this distribution key ?0 ?1 ?2 0.0 0.0 0.1 0.2 0.7 ?1 ?1 ?4 ?4 0 1 2 3 4 The process stops when END has the largest attention weights. ?2 ?2 ?3 ?3 ?4 ?4 ?1 ?1 ?0 ?0

8. https://arxiv.org/abs/1704.04368 Applications - Summarization

9. More Applications Machine Translation Chat-bot User: X Machine: