Linguistic Marker Discovery with BERT
Detecting politeness in text is a task that has attracted attention in recent years due to its role in identifying abusive language. Previous work have either used feature-based models or deep neural networks for this task. Due to the lack of context, feature-based models perform significantly worse compared to modern deep-learning models. We leverage pretrained Bert representations to provide clustering of words based on their context. We show how we are able to obtain interpretable contextualized features that can help reduce the gap in performance between feature-based models and deep learning approaches.