• python 2.7
• tensorflow 1.8.0

• Data Preporcessing --------- Data.py Data_Visualization.ipynb
• TFIDF model ---------------------- TFIDF.py
• LM model ---------------------- LM.py
• CNN model ---------------------- CNN_model.py CNN_train.py
• Inference ------------------ Inference.py

• Address origin data (Chinese stop words removal, QA-pair --> pred_QA-pair.csv)
• Generate CNN data (padding sentences 32 words each sentence and word embedding)

1. Data preprocessing and read pred data
2. Generate tfidf representation for each sentence
3. Caculate cosine similarity for question - question pair
4. Get the answer with the most similar question

1. Data preprocessing and read pred data
2. Caculate LM similarity for question - question pair
3. Get the answer with the most similar question

1. Data preprocessing and read pred data
2. Generate word embedding (initialize with baidu baike vector) representation
3. Train the CNN model with question - answer pair
4. Feed the question - answer pair and Get the most similar answer

• Input : question, top_k
• Output : TFIDF LM CNN top k response

plus: Data not uploaded beacuse of the privacy