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文章翻译

大约 3 分钟约 783 字

文章翻译

在我们日常学习中,经常会遇到不同语言的文字,我们想快速把它翻译成中文或者其他语言怎么办?今天我来教大家如何快速翻译

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视频教程

文字教程

打开智游剪辑open in new window,搜索文章翻译

我们选择目标语音和原文信息,点击开始翻译就可以自动进行翻译了

效果预览

原文

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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贡献者: 小游