In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.
@article{arxiv.1711.09684,
title = {Production Ready Chatbots: Generate if not Retrieve},
author = {Aniruddha Tammewar and Monik Pamecha and Chirag Jain and Apurva Nagvenkar and Krupal Modi},
journal= {arXiv preprint arXiv:1711.09684},
year = {2018}
}