Quora-Question-Pairs

Approach

Using MaLSTM model (Siamese networks + LSTM with Manhattan metric) to detect semantic similarity between question pairs. Training dataset used is a subset of the original Quora Question Pairs Dataset (~363K pairs used)

LINKS :

• Kaggle competition - https://www.kaggle.com/c/quora-question-pairs
• Complete dataset - https://www.kaggle.com/quora/question-pairs-dataset
• MaLSTM paper - http://www.mit.edu/~jonasm/info/MuellerThyagarajan_AAAI16.pdf

RESULTS :

Tried different architectures varying hyperparameter, such as optimize, number of hidden states of LSTM cell, activation function of LSTM cell, resulting in validation accuracy of ~77%-81%. Weights for most of the models were saved.

Pre-trained model used in the testing script has 81.2% validation accuracy. 15% data was used as the validation set.

Instructions for use of testing script :

• Ensure all libraries as mentioned in the requirements.txt file are downloaded

• Download Google's pre-trained Word2Vec embedding file and keep the extracted file in the same folder where this repo is cloned. Link : https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit

• Download the weight file, 'model30_relu_epoch_3.h5' and 'test.py' to your local system or you can download the entire repo using git clone

• The testing script can be run from the terminal as shown below :

                                     python test.py s1 s2
  

Here, s1 and s2 are the two sentences/questions which are being compared semantically. The output is a binary 1/0 implying the pair being duplicate/not duplicate.

Another example :

  python test.py "Who will be the next captain of Indian cricket team?" "Will Indian cricket team have a captain soon?"