MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues
This addresses data scarcity for companies wanting to implement task-oriented dialogue systems, though it is incremental as it adapts existing methods to a specific domain.
The study tackled the problem of data scarcity for task-oriented dialogue systems by investigating a novel approach to transform German monologue material, such as travel booking emails, into annotated dialogues suitable for training. The result showed that the generated dialogues and annotations were of high quality and could serve as a valuable starting point for training these systems.
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.