Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation

Tiziano Labruna, Bernardo Magnini

Abstract
Conversations exhibit significant variation when different styles are employed by participants, often leading to subpar performance when a dialogue model is exclusively trained on single-style datasets. We present a cost-effective methodology for generating multi-style conversations, which can be used in the development of conversational agents. This methodology only assumes the availability of a conversational domain, such as a knowledge base, and leverages the generative capabilities of large language models. In a pilot study focused on the generation aspect of task-oriented dialogues, we extended the well-known MultiWOZ dataset to encompass multi-style variations. Our findings highlight two key experimental outcomes: (i) these novel resources pose challenges for current single-style models, and (ii) multi-style resources enhance the dialogue model’s resilience to stylistic variations.
Anthology ID:
2024.lrec-main.1431
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
16473–16479
Language:
URL:
https://aclanthology.org/2024.lrec-main.1431
DOI:
Bibkey:
Cite (ACL):
Tiziano Labruna and Bernardo Magnini. 2024. Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16473–16479, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation (Labruna & Magnini, LREC-COLING 2024)
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