Opportunistic Active Learning for Grounding Natural Language Descriptions
Opportunistic Active Learning for Grounding Natural Language Descriptions
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发表时间：
2017-10
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通讯作者：
Jesse Thomason;Aishwarya Padmakumar;Jivko Sinapov;Justin W. Hart;P. Stone;R. Mooney
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作者：
Jesse Thomason;Aishwarya Padmakumar;Jivko Sinapov;Justin W. Hart;P. Stone;R. Mooney
主动学习从一个未标记的示例池中识别数据点，如果这些示例的标签可用，则最有可能改善监督模型的预测。大多数关于主动学习的研究都假设智能体可以访问整个未标记数据池，并且可以在初始训练阶段请求任何数据点的标签。然而，当合并到一个更大的任务中时，智能体可能只能查询未标记池的某个子集。代理还可以机会性地查询将来可能有用的标签，即使它们不是立即相关的。在本文中，我们证明了这种类型的机会主动学习可以提高性能接地自然语言描述的日常对象，一个重要的技能，为家庭和办公室机器人。我们发现，一个真实的机器人在一个对象识别设置，好奇的行为，询问用户的重要问题的含义，可能是偏离主题的当前对话，导致识别正确的对象更经常随着时间的推移。
Active learning identifies data points from a pool of unlabeled examples whose labels, if made available, are most likely to improve the predictions of a supervised model. Most research on active learning assumes that an agent has access to the entire pool of unlabeled data and can ask for labels of any data points during an initial training phase. However, when incorporated in a larger task, an agent may only be able to query some subset of the unlabeled pool. An agent can also opportunistically query for labels that may be useful in the future, even if they are not immediately relevant. In this paper, we demonstrate that this type of opportunistic active learning can improve performance in grounding natural language descriptions of everyday objects—an important skill for home and office robots. We find, with a real robot in an object identification setting, that inquisitive behavior—asking users important questions about the meanings of words that may be off-topic for the current dialog—leads to identifying the correct object more often over time.