论文阅读 Training Neural Machine Translation To Apply Terminology Constraints

时间 2021-01-02

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一. 该方法是在模型训练层面解决术语注入的问题。二. 训练阶段主要是改变数据的处理方式： 1. 原始数据层面（增加注释，0无关，1源语术语，2目标语言术语） 2.bpe层面将原始数据的注释推广到bpe切分后的token上，eg：如果Stellvertreter_2切分成了a、b,则a_2,b_2。 3.embedding层面将注释向量和词向量进行拼接。 4. 术语覆盖度问题为了保证没有包

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1. 论文阅读——Robust Unsupervised Neural Machine Translation with Adversarial Training
2. 论文阅读《NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE》
3. 论文阅读：《Neural Machine Translation by Jointly Learning to Align and Translate》
4. 【论文阅读】Neural Machine Translation By Jointly Learning To Align and Translate
5. 【论文阅读】Neural Machine Translation by Jointly Learning to Align and Translate
6. Neural machine translation by jointly learning to align and translate论文阅读笔记
7. 论文阅读：Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
8. 论文阅读：ByteNet, Neural Machine Translation in Linear Time
9. [论文阅读笔记] Massive Exploration of Neural Machine Translation Architectures
10. 论文阅读——Unsupervised Neural Machine Translation with Indirect Supervision

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