A self-hosted conversational chatbot built from a sequence-to-sequence (Seq2Seq) neural network: an encoder-decoder architecture whose encoder and decoder use LSTM recurrent cells, augmented with an attention mechanism so the decoder can weigh the most relevant input words when generating each reply token. Trained end to end on a purpose-built domain dataset (tourism in Draa-Tafilalet, Morocco) instead of relying on third-party NLP APIs, illustrating how specialized conversational models can serve niche domains without vendor lock-in.
This paper builds a domain-specific conversational chatbot using a sequence-to-sequence (Seq2Seq) neural model with an encoder-decoder architecture. The encoder and decoder use Long Short-Term Memory…