Papers Dialogue State Tracking
“Dialogue State Tracking” 태그가 달린 논문 311편 · 필터 해제
Candidate 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 TrackingSTREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media
Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisition pipelines face a persistent trilemma: expert annotation is expens…
Dialogue State TrackingReacTOD: 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 TrackingMultimodal Hidden Markov Models for Persistent Emotional State Tracking
Tracking an interpretable emotional arc of a conversation via the sentiment of individual utterances processed as a whole is central to both understanding and guiding communication in applied, especially clinical, conver…
Dialogue State TrackingEmotion RecognitionGEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking
Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models (LLMs) struggle despite their impressive general capabilities. We pr…
Computational EfficiencyDialogue State TrackingGraph Neural NetworkDynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encou…
Dialogue State TrackingContrastive LearningFrom Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents
Interactive tool-using agents must solve real-world tasks via multi-turn interaction with both humans and external environments, requiring dialogue state tracking, multi-step tool execution, while following complex instr…
Dialogue State TrackingReinforcement LearningJoint Speech and Text Training for LLM-Based End-to-End Spoken Dialogue State Tracking
End-to-end spoken dialogue state tracking (DST) is made difficult by the tandem of having to handle speech input and data scarcity. Combining speech foundation encoders and large language models has been proposed in rece…
Dialogue State TrackingDomain GeneralizationThe Speech-LLM Takes It All: A Truly Fully End-to-End Spoken Dialogue State Tracking Approach
This paper presents a comparative study of context management strategies for end-to-end Spoken Dialog State Tracking using Speech-LLMs. We systematically evaluate traditional multimodal context (combining text history an…
Dialogue State TrackingHybrid Dialogue State Tracking for Persian Chatbots: A Language Model-Based Approach
Dialogue State Tracking (DST) is an essential element of conversational AI with the objective of deeply understanding the conversation context and leading it toward answering user requests. Due to high demands for open-d…
Dialogue State TrackingAnswer GenerationIntent DetectionSlot FillingText-to-SQL Task-oriented Dialogue Ontology Construction
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separat…
Dialogue State TrackingTransfer LearningBeyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models
Large language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. However, conventional DST benchmarks primarily focus on stru…
Dialogue State TrackingFactors affecting the in-context learning abilities of LLMs for dialogue state tracking
This study explores the application of in-context learning (ICL) to the dialogue state tracking (DST) problem and investigates the factors that influence its effectiveness. We use a sentence embedding based k-nearest nei…
Dialogue State TrackingIn-Context LearningSentenceSentence Embedding+1Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully open-sourced and open-data components (…
Dialogue State TrackingInterpretable and Robust Dialogue State Tracking via Natural Language Summarization with LLMs
This paper introduces a novel approach to Dialogue State Tracking (DST) that leverages Large Language Models (LLMs) to generate natural language descriptions of dialogue states, moving beyond traditional slot-value repre…
Dialogue State Trackingslot-fillingSlot FillingTask-Oriented Dialogue SystemsLearning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings
Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-l…
Dialogue Act ClassificationDialogue State TrackingIntent DetectionLow-latency processingEnhancing LLM Reliability via Explicit Knowledge Boundary Modeling
Large language models (LLMs) frequently hallucinate due to misaligned self-awareness, generating erroneous outputs when addressing queries beyond their knowledge boundaries. While existing approaches mitigate hallucinati…
Dialogue State TrackingKnow Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling
Recent LLMs have enabled significant advancements for conversational agents. However, they are also well known to hallucinate, producing responses that seem plausible but are factually incorrect. On the other hand, users…
AI AgentDialogue State TrackingFrictionIntent-driven In-context Learning for Few-shot Dialogue State Tracking
Dialogue state tracking (DST) plays an essential role in task-oriented dialogue systems. However, user's input may contain implicit information, posing significant challenges for DST tasks. Additionally, DST data include…
Dialogue State TrackingIn-Context LearningLanguage ModelingLanguage Modelling+2Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking
Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains unseen at training time. Current large l…
Data AugmentationDialogue State TrackingDomain AdaptationLanguage Modeling+3