我們提出一個機器翻譯的方法，可以翻譯帶有命名實體的句子。
在研究方法中，我們將命名實體和其翻譯從雙語知識庫中抽出，以求正確翻譯在平行語料中代表性不足的命名實體。
此方法涉及偵測、鏈接和替換輸入句子中的命名實體，識別命名實體類別，以及訓練類神經機器翻譯模型並基於命名實體類別生成翻譯。
在執行時，接受文本段落，並將命名實體換成命名實體類別，然後用類神經機器翻譯模型和雙語知識庫生成翻譯。
我們將該方法應用於平行語料庫和雙語知識庫，實際製作了一個雛型翻譯系統。
根據我們對句子翻譯的評估，此模型在命名實體翻譯和增強目標句子的流利性方面上，有顯著的改進。

We introduce a method for learning to generate machine translation of a given sentence with potential rare named entities (NE).
In our approach, NEs and their translations are extracted from a bilingual knowledge base, aimed at maximizing correct translations for under-represented named entities in a parallel corpus.
The method involves linking NEs in the bilingual training sentences, replacing NEs with NE-type labels, and training a neural machine translation (NMT) model for partially lexicalized training data with regular tokens and NE-type labels.
At run time, the system accepts a text passage, links and replaces NEs with NE-type labels, and then translates the text using the trained NMT model and translate NE-type labels using a bilingual knowledge base.
We present a prototype system, WikiTrans that applies the method to a parallel corpus and \textit{Wikipedia}.
Evaluation on a set of sentences shows that the method achieves reasonably good performance in terms of generating high quality NE translations and enhancing the fluency of target sentences.

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