@inproceedings{steindl-etal-2025-monotodia,
title = "{M}ono{TOD}ia: Translating Monologue Requests to Task-Oriented Dialogues",
author = {Steindl, Sebastian and
Sch{\"a}fer, Ulrich and
Ludwig, Bernd},
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.33/",
doi = "10.18653/v1/2025.naacl-industry.33",
pages = "390--403",
ISBN = "979-8-89176-194-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues
%A Steindl, Sebastian
%A Schäfer, Ulrich
%A Ludwig, Bernd
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F steindl-etal-2025-monotodia
%X 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.
%R 10.18653/v1/2025.naacl-industry.33
%U https://aclanthology.org/2025.naacl-industry.33/
%U https://doi.org/10.18653/v1/2025.naacl-industry.33
%P 390-403
Markdown (Informal)
[MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues](https://aclanthology.org/2025.naacl-industry.33/) (Steindl et al., NAACL 2025)
ACL
- Sebastian Steindl, Ulrich Schäfer, and Bernd Ludwig. 2025. MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 390–403, Albuquerque, New Mexico. Association for Computational Linguistics.