1Undergraduate Student Department of Information Technology of Delhi Technological University, Shabad Daulatpur, Bawana Road, Delhi, 110042, India
2Professor Department of Information Technology of Delhi Technological University, Shabad Daulatpur, Bawana Road, Delhi, 110042, India
In current times, many people are suffering from anxiety-related problems. The problem is manifest in a developing country like India. To deal with this issue, we present an empathetic chatbot that can converse in the native language of Hindi based on the seq2seq encoder-decoder model with an attention mechanism to capture the context. Our model is trained on the benchmark Facebook Empathetic Dialogue dataset comprising of English dialogues. The resultant conversational agent can generate empathetic responses and conducts a friendly conversation with people suffering from mental health disorders. Since a vast population of Indians have Hindi as their mother tongue, this chatbot is adapted to converse in the Hindilanguage. The Google Translate API is used to perform the translation. To the best of our knowledge, no other paper has proposed such an idea of dealing with anxiety and mental stress by building an empathetic chatbot that can converse in the native Hindi language. Results obtained from our attention-based LSTM model is quite impressive and acts in favor of the argument of implementing such a chatbot at a practical level and facilitating extension to other languages. The BLEU score is used for comparing results and our attention-based conversational agent with GloVe word embedding gives a BLEU score of 0.143.
Hindi chatbot, Empathetic conversational agent, Sequence-to-sequence model, Attention mechanism, Facebook empathetic dialogue dataset
