Papers with TOD system

7 papers
“Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems (2025.coling-industry)

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Challenge: Detecting user frustration in task-oriented dialog systems is imperative for maintaining overall user satisfaction, engagement and retention.
Approach: They compare out-of-the-box methods for user frustration detection with open-source methods . they find an LLM-based approach is promising, as it captures both emotion and dialog breakdowns .
Outcome: The proposed method outperforms open-source methods in detecting user frustration in a TOD system.
An Adaptive Prompt Generation Framework for Task-oriented Dialogue System (2023.findings-emnlp)

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Challenge: Existing black-box large language models (LLMs) have excellent performance in task-oriented dialogue (TOD) tasks, but obtaining suitable prompts for specific tasks is challenging.
Approach: They propose a black-box large language model that generates domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation.
Outcome: The proposed framework outperforms existing prompting methods on the MultiWOZ 2.0 dataset.
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management (2021.findings-acl)

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Challenge: Task-oriented dialog systems typically manage structured knowledge to guide goal-oriented conversations.
Approach: They propose a TOD system with hybrid knowledge management, HyKnow, which extends the belief state to manage both structured and unstructured knowledge.
Outcome: The proposed model outperforms existing TOD systems in the evaluation of a multiWOZ dataset on unstructured knowledge with strong end-to-end performance.
Guideline Compliance in Task-Oriented Dialogue: The Chained Prior Approach (2025.findings-naacl)

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Challenge: Existing solutions based on large language models cannot achieve strict guideline compliance . a novel TOD system is being developed to improve guideline adherence .
Approach: They propose a task-oriented dialogue system that explicitly considers domain-specific guidelines by integrating a policy module.
Outcome: The proposed system achieves 20% better guideline compliance than state-of-the-art solutions.
An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)

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Challenge: Recent studies have shown that Large Language Models perform insufficiently as TOD systems.
Approach: They propose a self-checking mechanism to improve LLM performance as TOD systems.
Outcome: The proposed model outperforms existing models and improves their performance.
CaTER: A Framework for Context-aware Topology Entity Retrieval Contrastive Learning in End-to-End Task-Oriented Dialogue Systems (2025.findings-emnlp)

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Challenge: generative models suffer from implicit association preference, while retrieval-generation approaches face knowledge transfer discrepancies.
Approach: They propose a topology entity retrieval contrastive learning framework that uses context-aware distilling attention mechanism to suppress noise from weakly relevant attributes.
Outcome: The proposed framework outperforms strong baselines on three TOD benchmarks with small and large knowledge bases.
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)

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Challenge: a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions.
Approach: They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training .
Outcome: The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks .

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