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Nmt

Now only code for nmt based sent representation.

核心**

多语的句子表示,核心在于怎么利用多语对齐信息。 常用的方法包括bilingual nmt, multiligual nmt, etc. 具体地,

  1. 双语,独立训练en->de, de->en的翻译,把他们中间表示拿出来,连接作为任意句子的句子表示。实际上可能需要en->de->en。 即我这里sents_rep的思路。不方便能多语。
  2. 双语(多语), 每个语言配一个encode与一个decoder,期望对齐文本encoder表示的distance尽量小。 目前没有实现,有相关论文。应该也好实现。(更推荐思路)
  3. 还有一些不需要decoder的思路,即encoder表示尽量靠近。

所有这些表示可以与监督任务解耦合;可以通过分类任务评估效果;可以嵌入在其他任务里提升单语的效果(数据的迁移)。

sents_rep 期望使用之前训练好的2个nmt,利用1的思路,来做情感分析。 没有跑出结果&评估,代码应该是可以跑的,主要是需要提前训练出比较好的nmt模型。

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