Speech Act Patterns for Improving Generalizability of Explainable Politeness Detection Models

The lack of explainability in state-of-the-art Natural Language Understanding (NLU) classification models has increased interest in developing techniques for improving explainable linear feature-based models (e.g., Logistic Regression/SVM).Politeness detection is a task that exemplifies this interest.While those techniques perform well on the task when applied to data from the same domain as the training data, they lack generalizability and thus fall short when applied to data from other domains.This is due to their reliance on discovering domainspecific word-level features.We introduce a method for improving the generalizability of explainable politeness models by relying on speech act patterns instead of words, leveraging speech act labels assigned by the GPT-4 model.This approach goes beyond the mere words and injects intent into politeness classification models, enhancing their generalizability.Results demonstrate that the proposed method achieves state-of-the-art accuracy in the cross-domain setting among explainable methods, while falling short in the in-domain setting.Our findings illustrate that explainable models can benefit from Large Language Models.