What question did this study set out to answer?

To develop and evaluate an automated deep learning conversational system capable of generating timely, empathetic customer service responses on social media.

May 2, 2017

A New Chatbot for Customer Service on Social Media

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Authors

AXAnbang XuLinkedIn (United States)ZLZhe LiuNingbo University YGYufan GuoChinese University of Hong Kong

Discussion

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Implication

Model evaluation demonstrates effective empathetic response generation in social media inquiries, suggesting automated systems can match human agents in customer service.

Key Points

To develop and evaluate an automated deep learning conversational system capable of generating timely, empathetic customer service responses on social media.
Trained a deep learning conversational model on a dataset of nearly 1 million Twitter interactions between users and customer service agents across more than 60 commercial brands.
Evaluated the conversational model against an information retrieval baseline using both human evaluation scoring and automated evaluation metrics.
Characterization of user inquiries showed that over 40% of customer service requests on social media contained emotional expressions.
The conversational model demonstrated an empathetic response capability comparable to human agents when addressing emotional user situations.
The proposed deep learning system consistently outperformed the baseline information retrieval system across both human qualitative judgments and automatic evaluation metrics.

Cite This Study

Xu et al. (2017) studied this question.

synapsesocial.com/papers/69dd4ece99c691022d99c0e5 https://doi.org/10.1145/3025453.3025496

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Implication

Model evaluation demonstrates effective empathetic response generation in social media inquiries, suggesting automated systems can match human agents in customer service.

A New Chatbot for Customer Service on Social Media

Discussion

Member takes

Implication

Key Points

Cite This Study

A New Chatbot for Customer Service on Social Media

Discussion

Member takes

Implication

Key Points

Cite This Study

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