@inproceedings{ashby-etal-2024-towards-effective,
title = "Towards Effective Long Conversation Generation with Dynamic Topic Tracking and Recommendation",
author = "Ashby, Trevor and
Kulkarni, Adithya and
Qi, Jingyuan and
Liu, Minqian and
Cho, Eunah and
Kumar, Vaibhav and
Huang, Lifu",
editor = "Mahamood, Saad and
Minh, Nguyen Le and
Ippolito, Daphne",
booktitle = "Proceedings of the 17th International Natural Language Generation Conference",
month = sep,
year = "2024",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.inlg-main.43",
pages = "540--556",
abstract = "During conversations, the human flow of thoughts may result in topic shifts and evolution. In open-domain dialogue systems, it is crucial to track the topics discussed and recommend relevant topics to be included in responses to have effective conversations. Furthermore, topic evolution is needed to prevent stagnation as conversation length increases. Existing open-domain dialogue systems do not pay sufficient attention to topic evolution and shifting, resulting in performance degradation due to ineffective responses as conversation length increases. To address the shortcomings of existing approaches, we propose EvolvConv. EvolvConv conducts real-time conversation topic and user preference tracking and utilizes the tracking information to evolve and shift topics depending on conversation status. We conduct extensive experiments to validate the topic evolving and shifting capabilities of EvolvConv as conversation length increases. Un-referenced evaluation metric UniEval compare EvolvConv with the baselines. Experimental results show that EvolvConv maintains a smooth conversation flow without abruptly shifting topics; the probability of topic shifting ranges between 5{\%}-8{\%} throughout the conversation. EvolvConv recommends 4.77{\%} more novel topics than the baselines, and the topic evolution follows balanced topic groupings. Furthermore, we conduct user surveys to test the practical viability of EvolvConv. User survey results reveal that responses generated by EvolvConv are preferred 47.8{\%} of the time compared to the baselines and comes second to real human responses.",
}
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<abstract>During conversations, the human flow of thoughts may result in topic shifts and evolution. In open-domain dialogue systems, it is crucial to track the topics discussed and recommend relevant topics to be included in responses to have effective conversations. Furthermore, topic evolution is needed to prevent stagnation as conversation length increases. Existing open-domain dialogue systems do not pay sufficient attention to topic evolution and shifting, resulting in performance degradation due to ineffective responses as conversation length increases. To address the shortcomings of existing approaches, we propose EvolvConv. EvolvConv conducts real-time conversation topic and user preference tracking and utilizes the tracking information to evolve and shift topics depending on conversation status. We conduct extensive experiments to validate the topic evolving and shifting capabilities of EvolvConv as conversation length increases. Un-referenced evaluation metric UniEval compare EvolvConv with the baselines. Experimental results show that EvolvConv maintains a smooth conversation flow without abruptly shifting topics; the probability of topic shifting ranges between 5%-8% throughout the conversation. EvolvConv recommends 4.77% more novel topics than the baselines, and the topic evolution follows balanced topic groupings. Furthermore, we conduct user surveys to test the practical viability of EvolvConv. User survey results reveal that responses generated by EvolvConv are preferred 47.8% of the time compared to the baselines and comes second to real human responses.</abstract>
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%0 Conference Proceedings
%T Towards Effective Long Conversation Generation with Dynamic Topic Tracking and Recommendation
%A Ashby, Trevor
%A Kulkarni, Adithya
%A Qi, Jingyuan
%A Liu, Minqian
%A Cho, Eunah
%A Kumar, Vaibhav
%A Huang, Lifu
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F ashby-etal-2024-towards-effective
%X During conversations, the human flow of thoughts may result in topic shifts and evolution. In open-domain dialogue systems, it is crucial to track the topics discussed and recommend relevant topics to be included in responses to have effective conversations. Furthermore, topic evolution is needed to prevent stagnation as conversation length increases. Existing open-domain dialogue systems do not pay sufficient attention to topic evolution and shifting, resulting in performance degradation due to ineffective responses as conversation length increases. To address the shortcomings of existing approaches, we propose EvolvConv. EvolvConv conducts real-time conversation topic and user preference tracking and utilizes the tracking information to evolve and shift topics depending on conversation status. We conduct extensive experiments to validate the topic evolving and shifting capabilities of EvolvConv as conversation length increases. Un-referenced evaluation metric UniEval compare EvolvConv with the baselines. Experimental results show that EvolvConv maintains a smooth conversation flow without abruptly shifting topics; the probability of topic shifting ranges between 5%-8% throughout the conversation. EvolvConv recommends 4.77% more novel topics than the baselines, and the topic evolution follows balanced topic groupings. Furthermore, we conduct user surveys to test the practical viability of EvolvConv. User survey results reveal that responses generated by EvolvConv are preferred 47.8% of the time compared to the baselines and comes second to real human responses.
%U https://aclanthology.org/2024.inlg-main.43
%P 540-556
Markdown (Informal)
[Towards Effective Long Conversation Generation with Dynamic Topic Tracking and Recommendation](https://aclanthology.org/2024.inlg-main.43) (Ashby et al., INLG 2024)
ACL