# SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre

## TL;DR

This paper introduces SlugBot, a new computational dialogue model designed for open-domain conversations, supported by a novel ontological resource, aiming to advance chatbot capabilities beyond traditional models.

## Contribution

The paper presents a novel Discourse Relation Dialogue Model and its implementation within SlugBot, along with the creation of the UniSlug ontology to support this framework.

## Key findings

- Development of a new computational dialogue model
- Creation of the UniSlug ontological resource
- Implementation of the model in a modular system

## Abstract

One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure. Traditional computational models of dialogue are of two types: (1) task-oriented dialogue, supported by AI planning models,or simplified planning models consisting of frames with slots to be filled; or (2)search-oriented dialogue where every user turn is treated as a search query that may elaborate and extend current search results. Alexa Prize dialogue systems such as SlugBot must support conversational capabilities that go beyond what these traditional models can do. Moreover, while traditional dialogue systems rely on theoretical computational models, there are no existing computational theories that circumscribe the expected system and user behaviors in the intended conversational genre of the Alexa Prize Bots. This paper describes how UCSC's SlugBot team has combined the development of a novel computational theoretical model, Discourse Relation Dialogue Model, with its implementation in a modular system in order to test and refine it. We highlight how our novel dialogue model has led us to create a novel ontological resource, UniSlug, and how the structure of UniSlug determine show we curate and structure content so that our dialogue manager implements and tests our novel computational dialogue model.