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.
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...
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:2606. 20400v1 Announce Type: new Abstract: Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings.
The paper introduces statement normalization, a method that converts enterprise conversation transcripts into concise, speaker-attributed statements enriched with source references and semantic tags. This approach clarifies the text and enables focused retrieval of relevant evidence for specific analytical questions. Experiments on customer‑service calls show that normalization boosts classifier performance and, when combined with selection, benefits weaker prompted readers, all while allowing small models to handle the process efficiently.
The paper introduces a pipeline that generates intent‑labeled, two‑channel conversational speech from relational event lists, enabling controlled synthesis of full‑duplex dialogue with 42 phenomena across eight families in English and Mandarin. By having an LLM author each event’s speaker, text, conversational act, and attachment, and then aligning and timing these events independently, the system produces diverse, realistic turn‑taking signals. Experiments show that models trained on this synthetic corpus achieve higher floor‑occupancy accuracy and better start‑speaking/listening F1 scores compared to models trained on prior data.
The paper introduces ContraTalk, a benchmark that tests whether dialogue models truly use acoustic cues or rely on transcript shortcuts. It formalizes cross‑modal disagreement, creates conflict and consistent QA examples, and proposes an Audio Twin representation to expose acoustic evidence to models. Experiments show that while text‑only LLMs perform well on consistent cases, they falter on conflict cases, and AudioLLMs only partially mitigate this issue.
arXiv:2607. 16021v1 Announce Type: cross Abstract: Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems.
arXiv:2609.39225v1 Announce Type: new Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. Whil...
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.
arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.
arXiv:2606. 00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations.
The paper introduces a method called randomized intermediate guidance for training tandem speech-to-speech models, where a large language model (LLM) acts as a backend providing candidate responses while the user is speaking. Instead of simulating the backend’s guidance, the approach derives guidance directly from the conversation corpus, using target responses for informative guidance and randomly sampled responses to simulate irrelevant updates. Experiments on synthetic dialogues and 3.8k hours of real conversations show that this technique yields response quality comparable to LLM-generated baselines while improving natural turn‑taking and audio‑judge naturalness.