DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

TL;DR

DiSCoL introduces a dialogue system that uses conversational lines as controllable content-planning tools, enabling more engaging and topic-controlled open-domain conversations through transformer-based response generation.

Contribution

This paper presents DiSCoL, a novel approach that incorporates conversational lines to guide response generation, enhancing engagement and controllability in dialogue systems.

Findings

Convlines effectively guide response content and engagement.

User-controlled convlines allow topic steering.

Automatic and human evaluations confirm improved engagement.

Abstract

Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present \textbf{DiSCoL} (\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational \textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly \textbf{convlines}) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to \textit{control} the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.