SKG-Eval addresses the challenge of evaluating multi-turn dialogue systems by modeling dialogue as an evolving Semantic Knowledge Graph (SKG). This framework incrementally updates the graph through structured triple extraction to detect long-range issues like contradiction and inconsistency, offering improved evaluation beyond turn-isolated representations.
This content discusses Deep Dyna-Q, an approach that integrates planning for dialogue policy learning in conversational AI systems. The focus is on optimizing the task-completion process through spoken interaction with AI.
This paper introduces a proactive EMR assistant for doctor-patient dialogue, designed to overcome limitations of passive systems by integrating streaming ASR, belief stabilization, and action planning. The system was evaluated in a preliminary controlled setting, achieving an F1 of 0.84 and Recall@5 of 0.87.
The paper proposes a Forward-Focused Bidirectional Pseudo-Siamese Network (FF-BPSN) for planning dialogue paths in target-oriented proactive dialogue systems. This network uses identical transformer-based decoders for bidirectional planning and integrates information to construct a forward path, guiding language models in response generation.
This research introduces Inquisitive Conversational Agents (ICAs) designed to proactively extract information, specifically tailored for U.S. Supreme Court oral arguments. It proposes a Dual Hierarchical Reinforcement Learning framework to coordinate strategic dialogue management and fine-grained utterance generation, significantly outperforming baselines.
ToolWeave is a structured framework for synthesizing realistic multi-turn tool-calling dialogues, essential for LLMs to function as autonomous agents. It addresses challenges in existing synthetic data generation by supporting realistic multi-step workflows and reducing parameter hallucination.
ReacTOD introduces a bounded neuro-symbolic architecture for task-oriented dialogue systems, reformulating NLU as discrete tool calls within a self-correcting ReAct loop. It improves accuracy by up to 9.3 percentage points and achieves a 93.1% self-correction rate on intercepted errors.