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    <abstract>The construction of dialog systems for various types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD), has been an active area of research. In order to more closely mimic human-like conversations that often involve the fusion of different dialog modes, it is important to develop systems that can effectively handle both TOD and ODD and access different knowledge sources. In this work, we present a new automatic framework to enrich TODs with synthesized ODDs. We also introduce the PivotBot model, which is capable of handling both TOD and ODD modes and can access different knowledge sources to generate informative responses. Evaluation results indicate the superior ability of the proposed model to switch smoothly between TOD and ODD tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
%A Li, Miaoran
%A Peng, Baolin
%A Galley, Michel
%A Gao, Jianfeng
%A Zhang, Zhu (Drew)
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F li-etal-2023-enhancing-task
%X The construction of dialog systems for various types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD), has been an active area of research. In order to more closely mimic human-like conversations that often involve the fusion of different dialog modes, it is important to develop systems that can effectively handle both TOD and ODD and access different knowledge sources. In this work, we present a new automatic framework to enrich TODs with synthesized ODDs. We also introduce the PivotBot model, which is capable of handling both TOD and ODD modes and can access different knowledge sources to generate informative responses. Evaluation results indicate the superior ability of the proposed model to switch smoothly between TOD and ODD tasks.
%R 10.18653/v1/2023.sigdial-1.46
%U https://aclanthology.org/2023.sigdial-1.46
%U https://doi.org/10.18653/v1/2023.sigdial-1.46
%P 496-508

Markdown (Informal)

[Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog](https://aclanthology.org/2023.sigdial-1.46) (Li et al., SIGDIAL 2023)

• Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog (Li et al., SIGDIAL 2023)

ACL

• Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, and Zhu (Drew) Zhang. 2023. Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 496–508, Prague, Czechia. Association for Computational Linguistics.