Question answering system trained on Google's Natural questions dataset
The focus of this project is to build a question answering system using a Siamese network. Siamese network is used to get the best similarity from the question and answers embedding. In the Siamese network ,we used two different twin architectures, Bidirectional LSTM and BERT. We compare our results of both the architecture and evaluate it with state of art model.
Data set Used: Google Natural Question (https://ai.google.com/research/NaturalQuestions/download)
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