@article{Meng_Ren_Chen_Monz_Ma_de Rijke_2020, title={RefNet: A Reference-Aware Network for Background Based Conversation}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6370}, DOI={10.1609/aaai.v34i05.6370}, abstractNote={<p>Existing conversational systems tend to generate generic responses. Recently, Background Based Conversation (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, however, they either cannot generate natural responses or have difficulties in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address both issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel <em>reference decoder</em> that provides an alternative way to learn to directly select a <em>semantic unit</em> (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.</p>}, number={05}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Meng, Chuan and Ren, Pengjie and Chen, Zhumin and Monz, Christof and Ma, Jun and de Rijke, Maarten}, year={2020}, month={Apr.}, pages={8496-8503} }