Active Search using Meta-Bandits
Active Search using Meta-Bandits
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DOI：
10.1145/3340531.3417409
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Shengli Zhu;Jakob Coles;Sihong Xie
中科院分区：
文献类型：
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作者：
Shengli Zhu;Jakob Coles;Sihong Xie
在许多应用中，积极的实例很少，但识别起来很重要。例如，在自然语言处理中，给定关系的肯定句在大型语料库中很少。在这些应用程序中，积极的数据对于学习来说更有信息量，但在给一定量的数据贴上标签之前，人们不知道从哪里可以找到罕见的积极的数据。由于随机抽样可能会导致标记工作的显著浪费，以前的“主动搜索”方法使用单一的强盗模型来了解数据分布(探索)，同时从可能包含更多积极因素的区域进行抽样(利用)。许多强盗模型是可能的，次优模型降低了标记效率，但在任何数据被标记之前，最优模型是未知的。我们提出了Meta-AS(Meta Active Search)，它使用一个Meta-Bandit来评估一组基本盗贼，旨在有效地标记正例，而不是后见之明的最优基本盗贼。元强盗估计基本强盗的性能的均值和方差，并选择一个基本强盗来建议下一步标记哪些数据以进行勘探或利用。标签中的反馈更新了下一轮的基础土匪和元土匪。Meta-AS可以容纳一组不同的基本强盗来探索关于数据集的假设，而不会在标记开始之前过度承诺单一模型。在五个数据集上的关系抽取实验表明，Meta-AS比基本强盗和其他强盗选择策略更有效地进行正向标记。
There are many applications where positive instances are rare but important to identify. For example, in NLP, positive sentences for a given relation are rare in a large corpus. Positive data are more informative for learning in these applications, but before one labels a certain amount of data, it is unknown where to find the rare positives. Since random sampling can lead to significant waste in labeling effort, previous 'active search' methods use a single bandit model to learn about the data distribution (exploration) while sampling from the regions potentially containing more positives (exploitation). Many bandit models are possible and a sub-optimal model reduces labeling efficiency, but the optimal model is unknown before any data are labeled. We propose Meta-AS (Meta Active Search) that uses a meta-bandit to evaluate a set of base bandits and aims to label positive examples efficiently, comparing to the optimal base bandit with hindsight. The meta-bandit estimates the mean and variance of the performance of the base bandits and selects a base bandit to propose what data to label next for exploration or exploitation. The feedback in the labels updates both the base bandits and the meta-bandit for the next round. Meta-AS can accommodate a diverse set of base bandits to explore assumptions about the dataset, without over-committing to a single model before labeling starts. Experiments on five datasets for relation extraction demonstrate that Meta-AS labels positives more efficiently than the base bandits and other bandit selection strategies.