论文标题
抽样方法很重要：机器人语言获取的积极学习
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
论文作者
论文摘要
使用积极学习订购培训数据可以从较小的语料库中有效地进行学习。我们介绍了应用于不同复杂性的三种基础语言问题的主动学习方法的探索，以分析哪种方法适合提高学习效率。我们提出了一种分析此联合问题空间中数据复杂性的方法，并报告基础任务的特征以及设计决策（例如特征选择和分类模型）如何推动结果。我们观察到代表性以及多样性对于选择数据样本至关重要。
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.
