2020/10/28 by Kristian Miok, Miok, Kristian, Gregor Pirš +5
Computer Science · Mathematics · #Annotation #Artificial intelligence #Bayesian inference #Bayesian probability #Computation and Language (cs.CL) #Computer science #Context (archaeology) #Ensemble learning #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (stat.ML) #Machine learning #Natural language processing #Process (computing) #Spam and Phishing Detection #Topic Modeling #cs.CL #stat.ML
paper · pdf · doi:10.48550/arxiv.2010.14872
Accepted for COLING 2020, The 14th Linguistic Annotation Workshop
arxiv created 2020/10/28 · openalex publication_date 2020/10/28 · arxiv updated 2020/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.