# Conversational Document Prediction to Assist Customer Care Agents

arXiv: https://arxiv.org/abs/2010.02305 · HTML: https://mlsanity.com/paper/2010.02305/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval](https://mlsanity.com/paper/2112.07308/) (2021)
- [Does Dialog Length matter for Next Response Selection task? An Empirical Study](https://mlsanity.com/paper/2101.09647/) (2021)

## Abstract (arXiv)

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.