Abstract:
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.