PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation

Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya


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.
Anthology ID:
2022.coling-1.538
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6165–6175
Language:
URL:
https://aclanthology.org/2022.coling-1.538
DOI:
Bibkey:
Cite (ACL):
Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2022. PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6165–6175, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation (Firdaus et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.538.pdf