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Such development platforms however offer only limited functionality and customizability.

(Marutitech blog) A developer can choose to use a rule/retrieval based approach, here you simply write a pattern and a template.

Because of this the generated responses are prone to grammatical errors.

However once they are trained, the generative model outperforms the rule based approach especially in handling previously unseen queries.

Seq 2 Seq is a general purpose encoder decoder framework for Tensorflow which can be applied to machine translation, summarizing text ,image captioning and conversational modeling.

The seq2seq network connects two RNN's to work together to transform one sequence to another.

The final step in our model is implementing the Attention Mechanism.An encoder network condenses an input sequence into a vector while a decoder network unfolds that vector into a new sequence.Before training a dataset on a generative model we need to perform a preprocessing step called padding.This step is very important since it prevents the information loss that could have occured while compressing all the necessary information of a source sentence into a single vector.For source code, I recommend implementing Siraj Rawal's Seq 2 Seq Chatbot because of its clear documentation.

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