This is the readme file that contains the guidelines and information about the dataset and codes of the following paper
Paper Title: Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network
In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus, it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: ‘generic’ and ‘courteous’.
- Authors: Hitesh Golchha, Mauajama Firdaus, Asif Ekbal and Pushpak Bhattacharyya
- Affiliation: Indian Institute of Technology Patna, India
- Corresponding Author: Mauajama Firdaus (mauajama.pcs16@iitp.ac.in )
- Accepted: Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019
Data
- The data can be downloaded from CYCCD_data