@@ -554,9 +551,9 @@ Joint training for neural machine translation models with monolingual data
\parinterval 多语言单模型方法也可以被看做是一种迁移学习。多语言单模型方法能够有效地改善低资源神经机器翻译性能\upcite{DBLP:journals/tacl/JohnsonSLKWCTVW17,DBLP:conf/lrec/RiktersPK18,dabre2019brief}，尤其适用于翻译方向较多的情况，因为为每一个翻译方向单独训练一个模型是不实际的，不仅由于设备资源和时间上的限制，还由于很多翻译方向都没有双语平行数据。比如，要翻译100个语言之间的互译的系统，理论上就需要训练$100\times99$个翻译模型，代价是十分巨大的。这时就需要用到{\small\bfnew{多语言单模型方法}}\index{多语言单模型方法}（Multi-lingual Single Model-based Method\index{Multi-lingual Single Model-based Method}）。
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