通过统计的、面向目标的人机对话增加知识
Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog
摘要：
一些机器人可以使用自然语言与人类交互，并通过人机对话识别服务请求。然而，很少有机器人能够从这种经验中提高他们的语言能力。在本文中，我们开发了一个对话代理的机器人，能够解释用户的命令使用语义解析器，同时要求澄清问题，使用概率对话管理器。该对话代理能够通过从对话经验中学习来增强其知识库并提高其语言能力，例如，添加新实体并学习引用现有实体的新方法。我们已经在模拟中广泛评估了我们的对话系统，并通过MTurk和真实机器人平台与人类参与者进行了评估。我们证明，我们的对话代理执行更好的效率和准确性相比，基线学习代理。演示视频可以在https://youtu.be/DFB3jbHBqYE上找到
英文摘要：
Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabilities from this experience. In this paper, we develop a dialog agent for robots that is able to interpret user commands using a semantic parser, while asking clarification questions using a probabilistic dialog manager. This dialog agent is able to augment its knowledge base and improve its language capabilities by learning from dialog experiences, e.g., adding new entities and learning new ways of referring to existing entities. We have extensively evaluated our dialog system in simulation as well as with human participants through MTurk and real-robot platforms. We demonstrate that our dialog agent performs better in efficiency and accuracy in comparison to baseline learning agents. Demo video can be found at https://youtu.be/DFB3jbHBqYE
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。
