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Using Siamese LSTM to classify repeated quora questions. Attempted pretrained bert embeddings, Word2Vec and training own embeddings together with the model.

Jupyter Notebook 93.87% Python 6.13%
lstm-neural-networks siamese-neural-network keras python sentence-similarity quora-question-pairs

siamese-lstm-on-sentence-similarity's Introduction

quora-question-pairs

detection of question repeated questions

This is a sesond attempt at the Quora questions kaggle challange i worked on a few years back using classical features.
In this iteration I first attempt to use word2vec embeddings, then bert embedings, and finally training embeddings with the model.
The final model implemented is Siamese LSTM to classify pairs of sentences as either the same question or different.

Preprocessing

  • Removed special characters
  • Transform contractions into full form
  • Limit text to 16 words
  • padding for text length less than 16

Embeddings

  • Tried Word2Vec
  • Trained embedding together with model (with zero masking)

Model architecture

  • Input1 -> LSTM1(128)
  • Input2 -> LSTM2(128)
  • subtract[LSTM1, LSTM2]
  • Dense(128, ReLu) -> Dense(64, ReLu) -> Dense(1, Sigmoid)

Results

  • 40 epochs

  • loss: 0.5062 - acc: 0.7699

  • val_loss: 0.389 - val_acc: 0.823

  • improvements will be made, tune parameters

  • for inference check GetScore.py

  • Included Trained LSTM in rep, link to Word2vec embedding

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