Papers Task-Oriented Dialogue Systems
“Task-Oriented Dialogue Systems” 태그가 달린 논문 326편 · 필터 해제
Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems
This paper addresses the issue of the significant labor required to test interview dialogue systems. While interview dialogue systems are expected to be useful in various scenarios, like other dialogue systems, testing t…
Task-Oriented Dialogue SystemsCandidate Attended Dialogue State Tracking Using BERT
Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems. At each turn in a conversation, DST estimates the user belief or dialogue state, which is used as input for downstream module…
Task-Oriented Dialogue SystemsZero-shot GeneralizationDialogue State TrackingTRACER: Early Failure Detection for Task-Oriented Dialogue
Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present TRACER, a method for early failure dete…
Task-Oriented Dialogue SystemsReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
Task-oriented dialogue systems -- handling transactions, reservations, and service requests -- require predictable behavior, yet the moderately-sized LLMs needed for practical latency are prone to hallucination and forma…
Task-Oriented Dialogue SystemsDialogue State TrackingReasoning over Object Descriptions Improves Coreference Resolution in Task-Based Dialogue Systems
Task-based dialogue systems assist users in achieving specific goals, such as executing actions or retrieving information, through natural language interactions. Accurate coreference resolution is essential, as it involv…
Task-Oriented Dialogue SystemsCoreference ResolutionPrompt EngineeringSafety as Computation: Certified Answer Reuse via Capability Closure in Task-Oriented Dialogue
We introduce a new paradigm for task-oriented dialogue systems: safety certification as a computational primitive for answer reuse. Current systems treat each turn independently, recomputing answers via retrieval or gene…
Task-Oriented Dialogue SystemsExtracting Training Dialogue Data from Large Language Model based Task Bots
Large Language Models (LLMs) have been widely adopted to enhance Task-Oriented Dialogue Systems (TODS) by modeling complex language patterns and delivering contextually appropriate responses. However, this integration in…
Task-Oriented Dialogue SystemsLLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning
The reasoning capability of large language models (LLMs), defined as their ability to analyze, infer, and make decisions based on input information, is essential for building intelligent task-oriented dialogue systems. H…
Task-Oriented Dialogue SystemsLogical ReasoningFloCA: Towards Faithful and Logically Consistent Flowchart Reasoning
Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-specific flowchart to achieve a task goal. In this work, we formalize flowch…
Task-Oriented Dialogue SystemsResponse GenerationA Generative Model for Joint Multiple Intent Detection and Slot Filling
In task-oriented dialogue systems, spoken language understanding (SLU) is a critical component, which consists of two sub-tasks, intent detection and slot filling. Most existing methods focus on the single-intent SLU, wh…
Task-Oriented Dialogue SystemsSpoken Language UnderstandingMulti-Task LearningIntent DetectionDecoupling Strategy and Execution in Task-Focused Dialogue via Goal-Oriented Preference Optimization
Large language models show potential in task-oriented dialogue systems, yet existing training methods often rely on token-level likelihood or preference optimization, which poorly align with long-horizon task success. To…
Hierarchical Reinforcement LearningTask-Oriented Dialogue SystemsResponse GenerationATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems
Recent advances in task-oriented dialogue (TOD) systems, driven by large language models (LLMs) with extensive API and tool integration, have enabled conversational agents to coordinate interleaved goals, maintain long-h…
Task-Oriented Dialogue SystemsDialogue GenerationMMWOZ: Building Multimodal Agent for Task-oriented Dialogue
Task-oriented dialogue systems have garnered significant attention due to their conversational ability to accomplish goals, such as booking airline tickets for users. Traditionally, task-oriented dialogue systems are con…
Task-Oriented Dialogue SystemsDROID: Dual Representation for Out-of-Scope Intent Detection
Detecting out-of-scope (OOS) user utterances remains a key challenge in task-oriented dialogue systems and, more broadly, in open-set intent recognition. Existing approaches often depend on strong distributional assumpti…
Task-Oriented Dialogue SystemsIntent RecognitionIntent DetectionIntent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
Intent recognition is a fundamental component in task-oriented dialogue systems (TODS). Determining user intents and detecting whether an intent is Out-of-Scope (OOS) is crucial for TODS to provide reliable responses. Ho…
Task-Oriented Dialogue SystemsComputational EfficiencyIntent RecognitionSpec-TOD: A Specialized Instruction-Tuned LLM Framework for Efficient Task-Oriented Dialogue Systems
Task-oriented dialogue (TOD) systems facilitate goal-driven interactions between users and machines. While recent advances in deep learning have improved the performance, TOD systems often struggle in low-resource scenar…
Task-Oriented Dialogue SystemsEfficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing
Out-of-scope (OOS) intent detection is a critical challenge in task-oriented dialogue systems (TODS), as it ensures robustness to unseen and ambiguous queries. In this work, we propose a novel but simple modular framewor…
Task-Oriented Dialogue SystemsComputational EfficiencyIntent DetectionEmotionally Intelligent Task-oriented Dialogue Systems: Architecture, Representation, and Optimisation
Task-oriented dialogue (ToD) systems are designed to help users achieve specific goals through natural language interaction. While recent advances in large language models (LLMs) have significantly improved linguistic fl…
Task-Oriented Dialogue SystemsNatural Language UnderstandingAn Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals
Deep Reinforcement Learning (DRL) is widely used in task-oriented dialogue systems to optimize dialogue policy, but it struggles to balance exploration and exploitation due to the high dimensionality of state and action …
Deep Reinforcement LearningEvolutionary AlgorithmsTask-Oriented Dialogue SystemsWHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because users frequently do not …
Task-Oriented Dialogue Systems