TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles (2024.findings-acl)
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| Challenge: | Existing datasets for Task-Oriented Dialogs (TOD) lack consideration for adaptive response styles and neglect to simulate interactions with app contexts like calendars or alarms. |
| Approach: | They propose to generate an annotated task-oriented dialog dataset and an automatic pipeline to generate it. |
| Outcome: | The proposed dataset provides a variety of system response styles and provides verbose or non-verbal responses. |
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| Challenge: | Existing systems produce generic, monotonic responses that lack individuality and fail to adapt to users’ personal attributes. |
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AutoEval-ToD: Automated Evaluation of Task-oriented Dialog Systems (2025.naacl-long)
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| Challenge: | Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. |
| Approach: | They propose an automated end-to-end evaluation framework that interacts with the ToD system and then assesses its performance across key dimensions. |
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| Challenge: | User Simulators are used to train task-oriented dialogue systems . traditional training paradigms rely on human-engineered agendas resulting in generated responses that lack diversity and spontaneity. |
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TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
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Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
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| Challenge: | Existing task-oriented dialog datasets do not situate the dialog in the user’s multimodal context. |
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SQLWOZ: A Realistic Task-Oriented Dialogue Dataset with SQL-Based Dialogue State Representation for Complex User Requirements (2025.emnlp-main)
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| Challenge: | Existing TOD datasets present simplified interactions with simple slot-value style constraints and preferences. |
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Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)
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Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, Anna Korhonen
| Challenge: | Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence. |
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| Challenge: | Task-oriented dialogue systems focus on training multiple tasks such as language understanding, tracking states, and generating appropriate responses to help users achieve their specific goals. |
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I know you are different! Towards Persona Driven Knowledge-infused Dialogue Assistant (2026.eacl-long)
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| Challenge: | Task-Oriented Dialogue (TOD) systems often fall short in delivering personalized, context-rich responses, especially in low-resource, code-mixed, and multimodal settings like Hinglish. |
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JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset (2024.lrec-main)
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| Challenge: | Existing datasets for task-oriented dialogue systems in English are limited compared to Japanese. |
| Approach: | They evaluated the dialogue state tracking and response generation capabilities of Japanese language datasets using multi-domain task-oriented dialogues. |
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