@inproceedings{tao-etal-2024-making,
title = "Making a Long Story Short in Conversation Modeling",
author = "Tao, Yufei and
Mines, Tiernan and
Agrawal, Ameeta",
editor = {Hosseini-Kivanani, Nina and
H{\"o}hn, Sviatlana and
Anastasiou, Dimitra and
Migge, Bettina and
Soltan, Angela and
Dippold, Doris and
Kamlovskaya, Ekaterina and
Philippy, Fred},
booktitle = "Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)",
month = mar,
year = "2024",
address = "St Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.teicai-1.7",
pages = "42--49",
abstract = "Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impact of varied utterance lengths on the quality of subsequent responses generated by conversation models. Using GPT-3 as the base model, multiple dialogue datasets, and several metrics, we conduct a thorough exploration of this aspect of conversational models. Our analysis sheds light on the complex relationship between utterance lengths and the quality of follow-up responses generated by dialogue systems. Empirical findings suggests that, for certain types of conversations, utterance lengths can be reduced by up to 72{\%} without any noticeable difference in the quality of follow-up responses.",
}
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<abstract>Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impact of varied utterance lengths on the quality of subsequent responses generated by conversation models. Using GPT-3 as the base model, multiple dialogue datasets, and several metrics, we conduct a thorough exploration of this aspect of conversational models. Our analysis sheds light on the complex relationship between utterance lengths and the quality of follow-up responses generated by dialogue systems. Empirical findings suggests that, for certain types of conversations, utterance lengths can be reduced by up to 72% without any noticeable difference in the quality of follow-up responses.</abstract>
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%0 Conference Proceedings
%T Making a Long Story Short in Conversation Modeling
%A Tao, Yufei
%A Mines, Tiernan
%A Agrawal, Ameeta
%Y Hosseini-Kivanani, Nina
%Y Höhn, Sviatlana
%Y Anastasiou, Dimitra
%Y Migge, Bettina
%Y Soltan, Angela
%Y Dippold, Doris
%Y Kamlovskaya, Ekaterina
%Y Philippy, Fred
%S Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St Julians, Malta
%F tao-etal-2024-making
%X Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impact of varied utterance lengths on the quality of subsequent responses generated by conversation models. Using GPT-3 as the base model, multiple dialogue datasets, and several metrics, we conduct a thorough exploration of this aspect of conversational models. Our analysis sheds light on the complex relationship between utterance lengths and the quality of follow-up responses generated by dialogue systems. Empirical findings suggests that, for certain types of conversations, utterance lengths can be reduced by up to 72% without any noticeable difference in the quality of follow-up responses.
%U https://aclanthology.org/2024.teicai-1.7
%P 42-49
Markdown (Informal)
[Making a Long Story Short in Conversation Modeling](https://aclanthology.org/2024.teicai-1.7) (Tao et al., TEICAI-WS 2024)
ACL
- Yufei Tao, Tiernan Mines, and Ameeta Agrawal. 2024. Making a Long Story Short in Conversation Modeling. In Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024), pages 42–49, St Julians, Malta. Association for Computational Linguistics.