arXiv:2607. 07974v1 Announce Type: cross Abstract: Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems.
By Yihong Xu, Mingyu Kang, Linyuan L\"u
arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.
By Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
The paper evaluates whether zero‑shot large language models (LLMs) can replace fine‑tuned natural language understanding (NLU) classifiers for intent detection in conversational systems. Experiments on ATIS and CLINC150 show that fine‑tuned RoBERTa outperforms Claude Haiku zero‑shot when ample in‑domain labels are available, but the LLM matches the supervised model on the broader 150‑intent CLINC150 schema. The authors identify three production‑relevant scenarios where LLMs excel—out‑of‑scope detection, robustness to ASR noise, and dynamic per‑deployment schemas—and present a decision framework to guide practitioners.
whyItMatters:"The study provides concrete evidence and a practical framework for deciding when to deploy LLMs versus fine‑tuned models in real‑world conversational applications."
By Carson Rodrigues, Oysturn Vas
arXiv:2608.28926v1 Announce Type: cross
Abstract: We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynami...
By Galo Castillo-L\'opez, Alexis Lombard, Ga\"el de Chalendar, Nasredine Semmar
The paper introduces Aslema, a system for the NADI 2026 Shared Task 5, which includes intent recognition and slot filling. The authors evaluate four omni LLMs in zero‑shot and fine‑tuned settings, finding that fine‑tuning consistently outperforms zero‑shot inference. They further augment data by generating culturally grounded Tunisian Derja utterances with an LLM and synthetic speech via voice cloning, which improves performance; the final system based on Qwen3‑Omni‑30B achieves 86.8% intent accuracy and 34.7 WER on devtest, ranking 1st in slot filling and 4th in intent recognition on the official test set.
By Tajwaar Shafiq, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury
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.
By Matthew Sun, Vinay Kothapally, Meng Yu, Chao Huang, Hao Zhang, Yixuan Zhang, Steve Yves