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Physical Sciences › Computer Science › Artificial Intelligence
Attention Is All You Need
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Conflicts of Interest
Identified Weaknesses
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Paper Summary
Paperzilla title
Goodbye RNNs, Hello Attention: A Transformer's Tale of Parallel Training
This paper introduces the Transformer, a novel neural network architecture based solely on attention mechanisms, eliminating the need for recurrence and convolutions. The Transformer achieves state-of-the-art results on English-to-German and English-to-French machine translation tasks while requiring significantly less training time compared to previous models.
Possible Conflicts of Interest
Some authors were affiliated with Google Brain and Google Research.
Identified Weaknesses
Limited Task Diversity
The paper primarily focuses on the application of the Transformer model to machine translation tasks, with limited exploration of other applications. While the English constituency parsing experiment shows some generalization capability, more diverse tasks are needed to fully assess the model's versatility.
Handling Long Sequences
The paper acknowledges the potential limitations of self-attention for very long sequences and proposes restricting attention to a neighborhood. However, this approach is not explored in detail, leaving its effectiveness and implications for long-range dependencies unaddressed.
Memory Requirements
The paper compares the Transformer's computational complexity with recurrent and convolutional layers, but lacks a comprehensive analysis of its memory requirements. This is particularly important given the quadratic complexity of self-attention with respect to sequence length.
Rating Explanation
This paper introduces the Transformer, a novel architecture with significant impact on the field of NLP. Its use of self-attention instead of recurrence allows for increased parallelization and improved performance on machine translation tasks. While the paper primarily focuses on machine translation, the introduced concepts have wide applicability and have influenced numerous subsequent works. The limited exploration of other NLP tasks and the potential issues with very long sequences slightly lower the rating.
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File Information
Original Title:
Attention Is All You Need
File Name:
1706.03762v7.pdf
[download]
File Size:
2.11 MB
Uploaded:
July 08, 2025 at 12:14 PM
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