【论文阅读】Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-S
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采用拷贝与检索机制在序列预测的模型中生成自然语言的问题答案
为了生成自然语言答案，现有的方法通常利用一些了的NLP tools与归纳模板，这种方式覆盖度低，难以应对丰富的语言现象，文章将问答看做端到端的学习问题，在应对问答时，通过分析问题、在知识库中检索来生成连贯的正确地答案。
在解码阶段，不同于机器翻译，预测词并非都来自设定好的词表，而是分别来自词表、问题本身以及知识库，通过在网络中结合问句与知识库的信息，缩小了出词范围。
网络架构为：
Case Study：
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