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Status: Unchecked ×Keyword: constituency parsing ×Clear all

4 claims from 1 paper

1. Computer Science › Natural Language Processing Techniques

   Attention Is All You Need

   Vaswani, Shazeer, Parmar et al. · 2025

   The paper proposes the Transformer, a network built only on attention mechanisms, and reports better translation quality and shorter training times than leading recurrent or convolutional models.

   Unchecked4 claims
   Show 4 claims

   1. UncheckedThe Transformer model scored 28.4 BLEU on WMT 2014 English-to-German translation, over 2 BLEU above earlier best results, including ensembles.“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.”
   2. UncheckedOn WMT 2014 English-to-French translation, the Transformer reports a single-model BLEU of 41.8 after 3.5 days on eight GPUs, at a fraction of rivals' training cost.“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.”
   3. UncheckedThe paper reports that the Transformer, a model built only on attention, also worked well on English constituency parsing, with large and with limited training data.“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.”
   4. UncheckedOn two machine translation tasks, Transformer models are reported to give better quality, parallelise more easily and train in significantly less time.“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.”