Production Ready Chatbots: Generate if not Retrieve
This work addresses the challenge of building production-ready chatbots for specific domains like scheduling, though it is incremental in combining existing techniques.
The paper tackles the problem of creating robust chatbots for scheduling reminders by developing a hybrid system that combines a rule-based graph dialogue system with a neural conversational model, resulting in significant improvements over baseline rule-based approaches and handling complex queries effectively.
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.