Chatbot for Government Schemes Using SEQ2SEQ Model

Government schemes are programs created and implemented by the government combined with the goal of raising citizen standards of living and addressing problems within society. However, most people are unaware of these programs and cannot take benefit of them. The process for discovering about the various government programs is addressed by this chatbot. We may educate users of the various schemes that are available for them to utilize based on their particular needs with the aid of this chatbot. Sequential data that depended on statistical and phrase-based methods was handled by machine translation systems. However, it was unable to manage global context and long-distance interdependence. For this reason, this chatbot is implemented using a seq2seq model. The seq2seq model is given data about the various schemes, processes it using an encoder, and outputs the result to a decoder, generating the response. The Seq2seq model offers flexibility in handling data in sequence by utilizing the computational capacity of recurrent neural networks. This model can handle sequential data efficiently because of its encoder-decoder structure, which are able to handle input-output sequences of differing lengths. Users can provide prompts regarding the particular department they wish to find out more about by using this model. Based on the data fed to it regarding government schemes, this model will produce responses.