liqiang
b119135836
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68 lines
6.7 KiB
JSON
68 lines
6.7 KiB
JSON
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"加粗的 Transformer 一词",
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"最后一条关于 English constituency parsing 的泛化结论"
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"content": "{\"narration\":\"上一页我们看了论文标题，现在这一页是摘要，相当于整篇论文的电梯演讲。请大家先看屏幕上加粗的 Transformer 这个词——作者提出这是第一个完全建立在注意力机制上的模型，彻底抛弃了循环和卷积结构。为什么这一点关键？因为没有了循环，训练就可以高度并行，耗时大幅下降。再看屏幕下方两个数字：英德翻译 28.4 BLEU，比此前最好成绩高出 2 个点；英法 41.8 BLEU，直接刷新纪录。最后一行还提到，它能泛化到英语句法分析等其他任务。那么注意力机制究竟是如何运作的？我们下一页就从引言开始拆解。\",\"visual_references\":[\"页面标题 Abstract\",\"加粗的 Transformer 一词\",\"数值 28.4 BLEU（英德）与 41.8 BLEU（英法）\",\"最后一条关于 English constituency parsing 的泛化结论\"]}",
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