Implementation of semantic question matching with deep learning approaches mentioned in the blog of Quora.

This project includes 4 RNN models mentioned in the blog of quora. These models have a common structure, which can be summarised as a series steps:

• First generate word embeddings for each sentence.
• Second represent each word by using RNN network (could be LSTM or GRU or bidirectional RNN) which captures context of each sentence.
• Third use computational operations (simple concatenate or attend mechanisms or other ops) to combine several timesteps context vectors into a single vector. This step differentiate the 4 models in this project.
• At last use dense feed forward neural networks for the final classification.

models/basic_rnn.py - implements basic RNN, the first approach in quora blog.

models/res_distance.py - implements the second approach in quora blog.

models/decomposable_attention.py - implements an attention-based approach from [2], the third approach in quora blog

models/compare_aggregate.py - implements another attention-based approach from [3].

models/layers.py - other layers, word embedding layers, context layer, etc.

train_model.py - train and test models. You could change model_name in main() to train different model.

data_util.py - data processing.

config.py - hyper-parameters.

I also implement BiMPM [4] model, however the matching operations are very complicated which result in a very slow training time. You could find the codes from here.

If you find any bugs, please create an issue, thanks.

• python 2.7
• tensorflow 1.1.0
• keras 2.0.3
• numpy 1.12.1
• pandas 0.19.2
• nltk 3.2.2
• gensim 1.0.1

[1] Lili Jiang, Shuo Chang, and Nikhil Dandekar. "Semantic Question Matching with Deep Learning."

[2] Ankur P Parikh, Oscar Tackstrom, Dipanjan Das, and Jakob Uszkoreit. "A decomposable atten- tion model for natural language inference."

[3] Shuohang Wang and Jing Jiang. "A compare-aggregate model for matching text sequences."

[4] Zhiguo Wang, Wael Hamza and Radu Florian. "Bilateral Multi-Perspective Matching for Natural Language Sentences."