arXiv AI
Jul 20
Candidate Attended Dialogue State Tracking Using BERT
arXiv:2607. 16021v1 Announce Type: cross Abstract: Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems.
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 modules to predict system actions and generate responses.
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arXiv Computation and Language
Sep 1
Learning to Reason and Use Tools through Unsupervised Fine-Tuning in Task-Oriented Dialog Systems
arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...
arXiv Computation and Language
Sep 4
Decoupling Turn-Taking from Semantics: A Decoupled Data Approach for Finite-State-Machine-Based Full-Duplex Dialogue
The paper introduces a decoupled data approach for the Neural Finite State Machine (NFSM) framework to improve full‑duplex dialogue. It serializes real human‑human spoken dialogues into FSM tapes using a rule‑based event‑guided transformation, while shaping semantics through human‑agent text dialogues. A Source‑Aware Calibrated (SAC) loss is proposed to balance state‑transition token distribution and align each data source with its strongest supervisory signal, leading to better turn‑taking performance without sacrificing semantic quality.
arXiv AI
Jun 9
DYCP: Dynamic Context Pruning for Long-Form Dialogue with LLMs
arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.
arXiv AI
Sep 4
IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations
IRWOZ 2.0 is a refined dialogue dataset for industrial human‑robot interaction, expanding to 390 dialogues across four domains—Assembly, Delivery, Position, and Relocation. The dataset was improved using large language models (Mistral and Claude‑3.5) for generation and quality refinement, including manual corrections and automated typo removal. Benchmark tests show a substantial boost in dialogue state‑tracking performance, with GPT‑2’s BLEU‑4 score rising from 0.1651 to 0.5604 compared to the original IRWOZ.
arXiv Computation and Language
Aug 27
Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting
The paper introduces a method for forecasting conversational derailment by incorporating speech act information as an auxiliary signal to enhance pragmatic representations. This approach aims to reduce lexical noise and improve generalizability, especially in low-data and cross-domain scenarios. Experiments on three datasets demonstrate performance gains over existing methods.
arXiv AI
Aug 18
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
arXiv:2608. 15755v1 Announce Type: new Abstract: User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints.
arXiv AI
Aug 11
DialectS2S: End-to-End Speech Dialogue Modeling for Low-Resource Chinese Dialects
arXiv:2608. 08067v1 Announce Type: cross Abstract: Current end-to-end speech dialogue models are primarily optimized for mainstream languages and remain limited in low-resource dialect scenarios due to the scarcity of dialect speech data.
arXiv AI
Oct 7
PERSIST: Who-What-When Memory Across Sessions for Full-Duplex Spoken Dialogue
PERSIST is a persistent memory system designed for multi-session, multi-speaker spoken dialogue, explicitly modeling Who, What, and When to enable voice assistants to recall past conversations and adapt to individual users. It structures cross-session histories into readable event records and retrieves them using a 3W joint scoring mechanism that combines semantic content, acoustic speaker identity, and temporal state. By reusing intermediate representations from the dialogue backbone, PERSIST reduces retrieval latency from 578.42 ms to 7.03 ms and achieves 85.08% end‑to‑end task accuracy on the SpokenTrace benchmark, improving EM@3 from 49.01% to 82.10%.
arXiv AI
Jun 3
Efficient ASR Training with Conversations that Never Happened
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
Hugging Face Trending Papers
Aug 26
Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting
Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing approaches often struggle in low-data settings and t...
arXiv AI
Jul 10
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
arXiv:2601. 02871v3 Announce Type: replace Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion.