@inproceedings{7ee7263c27274e7c8253cfb43d11dde0,
title = "DYME: A Dynamic Metric for Dialog Modeling Learned from Human Conversations",
abstract = "With increasing capabilities of dialog generation methods, modeling human conversation characteristics to steer the dialog generation towards natural, human-like interactions has garnered research interest. So far, dialogs have mostly been modeled with developer-defined, static metrics. This work shows that metrics change within individual conversations and differ between conversations, illustrating the need for flexible metrics to model human dialogs. We propose DYME, a DYnamic MEtric for dialog modeling learned from human conversational data with a neural-network-based approach. DYME outperforms a moving average baseline in predicting the metrics for the next utterance of a given conversation by about 20%, demonstrating the ability of this new approach to model dynamic human communication characteristics.",
keywords = "Conversational metrics, Dialog modeling, Dialog systems, Natural language processing",
author = "{von Unold}, Florian and Monika Wintergerst and Lenz Belzner and Georg Groh",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92307-5_30",
language = "English",
isbn = "9783030923068",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "257--264",
editor = "Teddy Mantoro and Minho Lee and Ayu, {Media Anugerah} and Wong, {Kok Wai} and Hidayanto, {Achmad Nizar}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
}