【论文阅读】Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-S
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
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相关文章
- 1. 【论文阅读】Incorporating copying mechanisim in sequence-to-sequence learning
- 2. 论文阅读笔记：GENERATING NATURAL ADVERSARIAL EXAMPLES
- 3. 《Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects》阅读笔记
- 4. 论文阅读：Sequence Generation by Editing Prototype
- 5. 论文笔记《Incorporating Copying Mechanism in Sequence-to-Sequence Learning》
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- 9. Incorporating biological structure into machine learning models in biomedicine（论文阅读报告）
- 10. [ACL2016] Incorporating Copying Mechanism in Sequence-to-Sequence Learning