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Detecting out of policy speech (OOPS) content is important but difficult.While machine learning is a powerful tool to tackle this challenging task, itis hard to break the performance ceiling due to factors like quantity andquality limitations on training data and inconsistencies in OOPS definition anddata labeling. To realize the full potential of available limited resources, wepropose a meta learning technique (MLT) that combines individual models builtwith different text representations. We analytically show that the resultingtechnique is numerically stable and produces reasonable combining weights. Wecombine the MLT with a threshold-moving (TM) technique to further improve theperformance of the combined predictor on highly-imbalanced in-distribution andout-of-distribution datasets. We also provide computational results to show thestatistically significant advantages of the proposed MLT approach.  All authors contributed equally to this work.

Meta learning with language models: Challenges and opportunities in the classification of imbalanced text