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Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

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This abstract introduces the Transformer, a novel neural network architecture for sequence transduction tasks. It claims the Transformer surpasses existing models in machine translation by achieving higher BLEU scores on standard benchmarks (WMT 2014 English-to-German and English-to-French) while being faster and more parallelizable to train. The authors also demonstrate its effectiveness on English constituency parsing. While promising, remember that the authors created the Transformer, so their assessment might be biased. Consider the specific BLEU scores presented, but also be aware that BLEU doesn't fully capture translation quality, and the comparison to other models isn't fully detailed. To gain a more complete understanding, it would be beneficial to read the full paper and look for independent verification of its claims.

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**Summary:**

This passage is an abstract describing a new neural network architecture called "Transformer," which uses only attention mechanisms instead of recurrent or convolutional neural networks. The authors claim that the Transformer achieves superior performance in machine translation tasks, is more parallelizable, trains faster, and generalizes well to other tasks like English constituency parsing. They support their claims with specific BLEU scores on English-to-German and English-to-French translation benchmarks.

**Document type:**

Academic paper abstract or conference proceeding.

**Claims:**

* The Transformer architecture is superior in quality to existing sequence transduction models.
* The Transformer is more parallelizable than existing models.
* The Transformer requires significantly less training time than existing models.
* The Transformer achieves a BLEU score of 28.4 on the WMT 2014 English-to-German translation task, exceeding existing best results by over 2 BLEU.
* The Transformer achieves a BLEU score of 41.8 on the WMT 2014 English-to-French translation task, establishing a new single-model state-of-the-art.
* The Transformer generalizes well to other tasks (English constituency parsing).

**Implications:**

The claims imply a significant advancement in machine translation and potentially other sequence transduction tasks. The improved parallelization and faster training times suggest increased efficiency and reduced computational costs. The superior performance suggests that the attention mechanism is a more powerful approach than recurrent or convolutional networks for these tasks. The successful application to constituency parsing hints at broader applicability beyond translation.

**Biases:**

The authors, being the creators of the Transformer model, have a clear vested interest in presenting it favorably. This doesn't necessarily invalidate their claims, but it's crucial to consider that the abstract might highlight positive aspects while potentially downplaying limitations or comparing against less favorable baselines. The lack of details on the specific models used for comparison also introduces some bias in the evaluation; choices in those comparisons affect the context of the claimed "superiority." Additionally, focusing on BLEU score as the primary metric might bias the results, as BLEU doesn't capture all aspects of translation quality.

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