Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.
@article{arxiv.1808.03430,
title = {Lingke: A Fine-grained Multi-turn Chatbot for Customer Service},
author = {Pengfei Zhu and Zhuosheng Zhang and Jiangtong Li and Yafang Huang and Hai Zhao},
journal= {arXiv preprint arXiv:1808.03430},
year = {2018}
}