@inproceedings{yoon-etal-2025-beyond,
    title = "Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents",
    author = "Yoon, Yejin  and
      Son, Yuri  and
      So, Namyeong  and
      Kim, Minseo  and
      Cho, Minsoo  and
      Park, Chanhee  and
      Lee, Seungshin  and
      Kim, Taeuk",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.672/",
    pages = "13291--13317",
    ISBN = "979-8-89176-332-6",
    abstract = "Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two. Yet, real-world conversations naturally involve fluid transitions between these modes. To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. TACT supports both user- and agent-driven mode switches, enabling robust modeling of complex conversational dynamics.To evaluate an agent{'}s ability to initiate and recover from mode transitions, we propose two new metrics{---}Switch and Recovery.Models trained on TACT outperform baselines in both intent detection and mode transition handling. Moreover, applying Direct Preference Optimization (DPO) to TACT-trained models yields additionalgains, achieving 75.74{\%} joint mode-intent accuracy and a 70.1{\%} win rate against GPT-4o in human evaluation.These results demonstrate that pairing structurally diverse data with DPO enhances response quality and transition control, paving the way for more proactive and transition-aware conversational agents."
}