Grounding human-machine conversation in a document is an effective way to improve the performance of retrieval-based chatbots. However, only a part of the document content may be relevant to help select the appropriate response at a round. It is thus crucial to select the part of document content relevant to the current conversation context. In this paper, we propose a document content selection network (CSN) to perform explicit selection of relevant document contents, and filter out the irrelevant parts. We show in experiments on two public document-grounded conversation datasets that CSN can effectively help select the relevant document contents to the conversation context, and it produces better results than the state-of-the-art approaches. Our code and datasets are available at https://github.com/DaoD/CSN.
@article{arxiv.2101.08426,
title = {Content Selection Network for Document-grounded Retrieval-based Chatbots},
author = {Yutao Zhu and Jian-Yun Nie and Kun Zhou and Pan Du and Zhicheng Dou},
journal= {arXiv preprint arXiv:2101.08426},
year = {2021}
}