MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues

TL;DR

This paper presents a method to generate task-oriented dialogue data from German monologues by fine-tuning large language models, enabling training of dialogue systems with limited existing datasets.

Contribution

It introduces a novel approach to create annotated dialogue datasets from monologue material, reducing data scarcity for training TOD systems.

Findings

Generated dialogues are of high quality and valid for training.

Crowd evaluation confirms the usefulness of the data.

Public dataset availability supports future research.

Abstract

Data scarcity is one of the main problems when it comes to real-world applications of transformer-based models. This is especially evident for task-oriented dialogue (TOD) systems, which require specialized datasets, that are usually not readily available. This can hinder companies from adding TOD systems to their services. This study therefore investigates a novel approach to sourcing annotated dialogues from existing German monologue material. Focusing on a real-world example, we investigate whether these monologues can be transformed into dialogue formats suitable for training TOD systems. We show the approach with the concrete example of a company specializing in travel bookings via e-mail. We fine-tune state-of-the-art Large Language Models for the task of rewriting e-mails as dialogues and annotating them. To ensure the quality and validity of the generated data, we employ crowd workers to evaluate the dialogues across multiple criteria and to provide gold-standard annotations for the test dataset. We further evaluate the usefulness of the dialogues for training TOD systems. Our evaluation shows that the dialogues and annotations are of high quality and can serve as a valuable starting point for training TOD systems. Finally, we make the annotated dataset publicly available to foster future research.