An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems
This work addresses the problem of maintaining dialogue context in domain-specific chatbots for banking and finance, though it appears incremental as it builds on existing ontology and linguistic methods.
The authors tackled the challenge of dialogue state tracking in banking and finance chatbots by introducing an ontology-based dialogue manager (OntoDM) that uses domain ontologies to manage conversation state, resolve anaphora, and drive interactions, but no concrete results or metrics are provided as the work is still in progress.
Keeping the dialogue state in dialogue systems is a notoriously difficult task. We introduce an ontology-based dialogue manage(OntoDM), a dialogue manager that keeps the state of the conversation, provides a basis for anaphora resolution and drives the conversation via domain ontologies. The banking and finance area promises great potential for disambiguating the context via a rich set of products and specificity of proper nouns, named entities and verbs. We used ontologies both as a knowledge base and a basis for the dialogue manager; the knowledge base component and dialogue manager components coalesce in a sense. Domain knowledge is used to track Entities of Interest, i.e. nodes (classes) of the ontology which happen to be products and services. In this way we also introduced conversation memory and attention in a sense. We finely blended linguistic methods, domain-driven keyword ranking and domain ontologies to create ways of domain-driven conversation. Proposed framework is used in our in-house German language banking and finance chatbots. General challenges of German language processing and finance-banking domain chatbot language models and lexicons are also introduced. This work is still in progress, hence no success metrics have been introduced yet.