Conversational Document Prediction to Assist Customer Care Agents

• URL: http://arxiv.org/abs/2010.02305v1
• Date: Mon, 5 Oct 2020 19:53:41 GMT
• Title: Conversational Document Prediction to Assist Customer Care Agents
• Authors: Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras and David Konopnicki
• Abstract summary: We study the task of predicting the documents that customer care agents can use to facilitate users' needs. We investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task.
• Score: 24.759188825018665
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents that customer care agents can use to facilitate users' needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.

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