SCOUT：一个情境化多模态人机对话语料库
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
AI总结：
本文提出 SCOUT 多模态人机对话语料库，基于 Wizard-of-Oz 协作探索实验构建，含 278 段对话及 AMR、Dialogue-AMR 和 Transactional Units 标注，用于研究人类如何与机器人交谈并推动自主导航对话系统发展。
摘要：
我们介绍了情境化理解事务语料库（SCOUT），这是一个在协作探索任务领域中的人机对话多模态集合。该语料库由多个 Wizard-of-Oz 实验构建而成，在实验中，人类参与者向远程机器人发出语言指令，使其移动并收集周围环境的信息。SCOUT 包含来自 278 段对话的 89,056 条话语和 310,095 个词，平均每段对话有 320 条话语。这些对话与实验期间可用的多模态数据流对齐：5,785 张图像和 30 张地图。该语料库已使用 Abstract Meaning Representation 和 Dialogue-AMR 进行标注，以识别说话者的话语意图和含义，并使用 Transactional Units and Relations 来跟踪话语之间的关系，从而揭示对话结构的模式。我们描述了该语料库及其标注如何被用于开发自主人机系统，并推动关于人类如何与机器人交谈的开放问题的研究。我们发布此语料库，以加速自主、情境化人机对话的研究进展，尤其是在需要发现环境细节的导航任务背景下。
英文摘要：
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
