Challenge: Existing TOD datasets present simplified interactions with simple slot-value style constraints and preferences.
Approach: They propose a novel TOD dataset that captures complex user requirements using SQL statements.
Outcome: The proposed dataset captures complex, real-world user requirements.

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GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems (2022.acl-long)

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Challenge: Existing multilingual task-oriented dialogue datasets lack high-quality data curation due to the high expense and challenges of human annotation.
Approach: They propose a method that generates a multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems.
Outcome: The proposed method generates a large-scale multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems.
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator (2022.findings-emnlp)

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Challenge: Task-oriented dialogue systems are drawing more attention in recent studies . current evaluation methods use annotated utterances in multi-turn dialogue sessions .
Approach: They propose an interactive evaluation framework for task-oriented dialogue (TOD) they build a goal-oriented user simulator based on pre-trained models and use it to generate dialogues .
Outcome: The proposed evaluation framework achieves 98% inform and success rates in the multi-woz dataset and the proposed scores measure the response quality besides the inform and successful rates.
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset (2020.tacl-1)

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Challenge: Despite the significant contributions to the community, there is still a gap between existing dialogue corpora and real-life human dialogue data.
Approach: They propose to develop Chinese cross-domain wizard-of-oz task-oriented dataset CrossWOZ with rich annotations of dialogue states and dialogue acts on both user and system sides.
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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.
Outcome: The proposed dataset provides a benchmark that is on par with MultiWOZ2.2 and the latest large language model (LLM)-based methods.
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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Challenge: Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence.
Approach: They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems.
Outcome: The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems.
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)

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Challenge: a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information .
Approach: They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system.
Outcome: The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains.
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)

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Challenge: Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots.
Approach: They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios.
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Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)

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Challenge: Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas.
Approach: They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models .
Outcome: The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks .
Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)

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Challenge: Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice .
Approach: They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query.
Outcome: The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets.
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue (2022.findings-naacl)

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Challenge: NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design .
Approach: They propose to use NLU++ to provide a more challenging evaluation environment for dialogue NLU models.
Outcome: The proposed dataset improves existing datasets and provides a much more challenging evaluation environment for dialogue NLU models.

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