Mixing It Up: Inducing Empathy and Politeness using Multiple Behaviour-aware Generators for Conversational Systems

摘要

significantly depends on their ability to effectively communicate with the users. Thus, the research Politeness is a key component that can assist in in recent times has been inclined towards modulatbuilding a strong customer-agent relationship. ing biases, styles, and control in text generation to With the ongoing increase in customer-care sysimprove these interactions. tems, it is crucial to have healthy relations with the users providing satisfaction and a better cusCustomer care is a typical application area for tomer experience. In this regard, it is significant automated task-oriented conversational systems. to model the different polite behaviors in an These systems provide potentially cost-effective agent to help the user in reaching the intended and reliable solutions for customer care. Nowadays, objectives. In our current work, we propose companies rely heavily on customer care service to the task of polite behavior-aware generation successfully guide and assist customers and estabconsidering the affective state of the user and lish long-term and healthy relationships with them. the conversational context. We design a Transformer based encoder-decoder framework with Customers generally interact with conversational three major components i.e., Affective tracker, agents with diverse expectations and attitudes (FølBehaviour-aware generators, and Polite genstad and Skjuve, 2019). In such situation, if the erator. The affective tracker is a context enagent fails to satisfy their expectations or fix their coder that captures the contextual information issue as desired, they may get frustrated or furialong with the affective information in the utous quickly (Jain et al., 2018). To avoid this, it terances; the behavior-aware generators indeis imperative for customer care agents to mimic pendently attends to the context information to compute behavior-aware polite representahuman-like (Fink, 2012) behaviour during the contions and finally, polite generator generates the versation. final polite response considering the represenPoliteness and empathy, which encompass sotations from different generators. Experimental cial belongingness are a few fundamental socioresults on the CYCCD dataset prove that our linguistic cues of humans (Brown et al., 1987; approach generates contextually correct and relMaslow, 1943). The use of social language by the evant responses compared to the state-of-the-art agents facilitates building rapport and emotional approaches and the baselines. connection with the customers. Politeness has been investigated recently (Golchha et al., 2019; Firdaus