Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
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DOI：
10.1109/bigdata50022.2020.9378415
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发表时间：
2020-11
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影响因子：
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通讯作者：
Nisha Pillai;Edward Raff;Francis Ferraro;Cynthia Matuszek
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文献类型：
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作者：
Nisha Pillai;Edward Raff;Francis Ferraro;Cynthia Matuszek
使用主动学习对训练数据的选择排序可以提高从较小的语料库中学习的效率。为了分析哪些方法适合于提高学习中的数据效率，我们提出了一种应用于三个不同复杂性的基础语言问题的主动学习方法的探索。我们提出了一种在这个联合问题空间中分析数据复杂性的方法，并报告了底层任务的特征以及设计决策（如特征选择和分类模型）如何驱动结果。我们观察到，代表性，随着多样性，是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.