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IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations

2026-09-03 16:08:57
Chen Li, Dimitrios Chrysostomou
arXiv_AI
                 
Abstract

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 this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2609.04030

PDF

https://arxiv.org/pdf/2609.04030.pdf

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