add an article for text generation.

README.cn.md

@@ -23,7 +23,7 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
 
 在利用语言模型生成文本的任务中，我们重点介绍循环神经网络语言模型，大家可以通过文档中的使用说明快速适配到自己的训练语料，完成自动写诗、自动写散文等有趣的模型。
 
-- 2.1 [使用循环神经网络语言模型生成文本](https://github.com/PaddlePaddle/models/tree/develop/generate_sequence_by_rnn_lm)
+- 2.1 [使用循环神经网络语言模型生成文本](https://github.com/PaddlePaddle/models/tree/develop/text_generation/generate_sequence_by_rnn_lm)
 
 ## 3. 点击率预估
 
@@ -75,8 +75,8 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
 
 - 8.1 [无注意力机制的神经机器翻译](https://github.com/PaddlePaddle/models/tree/develop/nmt_without_attention/README.cn.md)
 - 8.2 [使用Scheduled Sampling改善翻译质量](https://github.com/PaddlePaddle/models/tree/develop/scheduled_sampling)
-- 8.3 [带外部记忆机制的神经机器翻译](https://github.com/PaddlePaddle/models/tree/develop/mt_with_external_memory)
-- 8.4 [生成古诗词](https://github.com/PaddlePaddle/models/tree/develop/generate_chinese_poetry)
+- 8.3 [带外部记忆机制的神经机器翻译](https://github.com/PaddlePaddle/models/tree/develop/text_generation/nmt_with_external_memory)
+- 8.4 [生成古诗词](https://github.com/PaddlePaddle/models/tree/develop/text_generation/generate_chinese_poetry)
 
 ## 9. 阅读理解
 

README.md

@@ -20,7 +20,7 @@ In the example of word vectors, we show how to use Hierarchical-Sigmoid and Nois
 
 The language model is important in the field of natural language processing. In addition to getting the word vector (a by-product of language model training), it can also help us to generate text. Given a number of words, the language model can help us predict the next most likely word. In the example of using the language model to generate text, we focus on the recurrent neural network language model. We can use the instructions in the document quickly adapt to their training corpus, complete automatic writing poetry, automatic writing prose and other interesting models.
 
-- 2.1 [Generate text using the RNN language model](https://github.com/PaddlePaddle/models/tree/develop/generate_sequence_by_rnn_lm)
+- 2.1 [Generate text using the RNN language model](https://github.com/PaddlePaddle/models/tree/develop/text_generation/generate_sequence_by_rnn_lm)
 
 ## 3. Click-Through Rate prediction
 The click-through rate model predicts the probability that a user will click on an ad. This is widely used for advertising technology. Logistic Regression has a good learning performance for large-scale sparse features in the early stages of the development of click-through rate prediction. In recent years, DNN model because of its strong learning ability to gradually take the banner rate of the task of the banner.