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README.md

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 # SC-LSTM
-We focus on text generation, witch is a challenge task that generates a long text under the theme of multiple words. In detail, given a set W = {w1, w2, ..., wk}, this generator aims at generates a text under the semantic information of those words. SC-LSTM ([Wen et al., 2015](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP199.pdf)) is the best paper of EMNLP 2015, which is is a statistical language generator based on a semantically controlled Long Short-term Memory structure for response generation. The author incorporates a dialogue act 1-hot vector into the original LSTM model and enables the generator to output the word-related text. We directly use this model for our task. And we input a set of words represented by 1-hot vector instead of dialogue act vector in our task.
+We focus on text generation, witch is a challenge task that generates a long text under the theme of multiple words. In detail, given a set W = {w1, w2, ..., wk}, this generator aims at generates a text under the semantic information of those words. SC-LSTM ([Wen et al., 2015](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP199.pdf)) is the best paper of EMNLP 2015, which is is a statistical language generator based on a semantically controlled Long Short-term Memory structure for response generation. The author incorporates a dialogue act 1-hot vector into the original LSTM model and enables the generator to output the word-related text. We directly use this model for our task. And we input a set of words represented by 1-hot vector instead of dialogue act vector in our task.
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+The code in this repository is written in Python 2.7/TensorFlow 0.12. And if you use other versions of Python or TensorFlow, you should modify some code. Since SC-LSTM is based on original LSTM, we modify some code based on BasicLSTMCell class of TensorFlow to develop SC-LSTM model (detail in SC_LSTM_Model.py).