Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents

Official Repository for "Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents."

Project Page · Paper (ACL Anthology) · Paper (arXiv)

Yejin Yoon, Yuri Son, Namyoung So, Minseo Kim, Minsoo Cho, Chanhee Park, Seungshin Lee and Taeuk Kim. Accepted to EMNLP2025 long paper.

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🆕 Hugging Face Datasets Hub

TACT is now available via Hugging Face Datasets! Check it out here: HYU-NLP/TACT

You can easily load the dataset using HF datasets library:

from datasets import load_dataset

tact_multiwoz = load_dataset("HYU-NLP/TACT", data_dir="TACT_multiwoz")
print(tact_multiwoz["test"][0])
tact_slurp = load_dataset("HYU-NLP/TACT", data_dir="TACT_slurp")
print(tact_slurp["test"][0])

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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.

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Repository Structure

./
├── baselines/
│   └── ICL/
├── datasets/
│   ├── TACT_multiwoz/
│   └── TACT_slurp/
├── dialogue/
│   ├── generation/
│   └── validation/
├── docs/
└── evaluation/
    ├── gui/
    └── winrate_evaluation/

Citation

@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, Namyoung  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://aclanthology.org/2025.emnlp-main.672/",
    doi = "10.18653/v1/2025.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."
}

Yejin Yoon, Yuri Son, Namyoung So, Minseo Kim, Minsoo Cho, Chanhee Park, Seungshin Lee, and Taeuk Kim. 2025. Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13291–13317, Suzhou, China. Association for Computational Linguistics.

Log

• 2025.12.23 TACT is now available in this repository.
• 2025.12.24 Added baselines(ICL), dialogue generation/validation utilities, and evaluation modules.

License

TACT is derived from publicly available datasets, including SLURP and MultiWOZ.

• The text portion of SLURP is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
• MultiWOZ is released under the MIT License.

Accordingly:

• The TACT dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
• All code and scripts in this repository are released under the MIT License.

Users must provide appropriate attribution when using the dataset.