发表机构
Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IRWOZ初始版本对话状态与话语噪声导致状态跟踪准确率受限的问题，提出IRWOZ 2.0数据集，结合Mistral/Claude-3.5的LLM增强生成与质量优化，经实验使GPT-2的BLEU-4得分大幅提升，已公开以支撑工业HRI研究。
AI 中文摘要
IRWOZ通过领域特定注释改进了工业人机交互（HRI）对话系统，但初始版本的对话状态和话语存在大量噪声，限制了状态跟踪准确率。本文介绍IRWOZ 2.0，该版本通过大语言模型（LLM）增强生成（Mistral/Claude-3.5）及质量优化解决了上述局限。改进后的数据集扩展至4个工业领域（装配、配送、定位、重定位）的390段对话，包含人工修正及自动错别字移除。对话状态跟踪的基准实验显示出显著提升，与原始IRWOZ相比，GPT-2的BLEU-4得分从0.1651提升至0.5604。为支持工业HRI研究，我们在该公开网址发布了IRWOZ 2.0数据集。
英文摘要
IRWOZ has improved industrial human-robot interaction (HRI) dialogue systems through domain-specific annotations. However, its initial version contains substantial noise in dialogue states and utterances, limiting state-tracking accuracy. We introduce IRWOZ 2.0, which addresses these limitations through large language model (LLM) enhanced generation (Mistral/Claude-3.5) and quality refinements. Our improved dataset expands to 390 dialogues across 4 industrial domains (Assembly, Delivery, Position, Relocation), featuring manual corrections and automated typo removal. Benchmark experiments on dialogue state tracking demonstrate significant improvements, with GPT-2's BLEU-4 score increasing from 0.1651 to 0.5604 compared to original IRWOZ. To support industrial HRI research, we publicly released IRWOZ 2.0 dataset at https://ieee-dataport.org/documents/irwoz-20-large-language-model-driven-dialogue-dataset-industrial-robot-conversations
