Predicting Politeness Variations in Goal-Oriented Conversations

Politeness is an expression in language that eases the conversation toward a positive undertone. If there is a display of rudeness, even the finest communication can fall through. In addition, if lathered with politeness, even the most angst-prone scenario can be expressed with far less hurt. In this article, we address the task of identifying politeness in goal-oriented dialog systems. In this regard, we create politeness-annotated conversational data (PACD) utilizing Microsoft Dialogue Challenge and DSTC1 datasets. For correctly identifying the politeness, we employ a hierarchical transformer network that effectively captures the contextual information (i.e., previous utterances) and current input for predicting the politeness in a given utterance of a dialog. Empirical results demonstrate that our proposed approach outperforms all the defined baselines. Furthermore, through in- and cross-domain experiments, we show the necessity of a PACD to mitigate acts such as rude requests or insults for both socially interactive and task-oriented dialog systems.