PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation
Year: 2022
Type: other
Abstract: The interaction between a consumer and the customer service representative greatly contributes to the overall customer experience. Therefore, to ensure customers' comfort and retention, it is important that customer service agents and chatbots connect with users on social, cordial and empathetic planes. In the current work, we automatically identify the sentiment of the user and transform the neutral responses into polite responses conforming to the sentiment and the conversational history. Our technique is basically a reinforced multi-task network- the primary task being 'polite response generation' and the secondary task being 'sentiment analysis'- that uses a Transformer based encoder-decoder. We use sentiment annotated conversations from Twitter as the training data. The detailed evaluation shows that our proposed approach attains superior performance compared to the baseline models.
Source: Underline Science Inc.
DOI: 10.48448/4xtj-6y41
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6929283216