Bayesian Methods for Semi-supervised Text Annotation
This addresses annotation quality issues for NLP researchers and practitioners, but it is incremental as it builds on existing Bayesian and semi-supervised techniques.
The paper tackles the problem of inconsistent human text annotations by proposing two Bayesian semi-supervised methods to guide the annotation process, resulting in improved annotations and BERT model prediction performance in hate speech detection experiments.
Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assign different labels to a given text, the quality of produced annotations frequently varies. This is especially the case if decisions are difficult, with high cognitive load, requires awareness of broader context, or careful consideration of background knowledge. To alleviate the problem, we propose two semi-supervised methods to guide the annotation process: a Bayesian deep learning model and a Bayesian ensemble method. Using a Bayesian deep learning method, we can discover annotations that cannot be trusted and might require reannotation. A recently proposed Bayesian ensemble method helps us to combine the annotators' labels with predictions of trained models. According to the results obtained from three hate speech detection experiments, the proposed Bayesian methods can improve the annotations and prediction performance of BERT models.