An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems

TL;DR

This paper presents OntoDM, an ontology-based dialogue management system designed for banking and finance chatbots, leveraging domain ontologies to improve conversation state tracking, anaphora resolution, and domain-specific dialogue control.

Contribution

The paper introduces a novel ontology-based dialogue management framework tailored for banking and finance, integrating linguistic methods with domain knowledge for improved dialogue coherence.

Findings

Implemented in German banking chatbots

Combines linguistic methods with domain ontologies

Addresses language processing challenges in finance domain

Abstract

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.