Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.
@article{arxiv.1806.07042,
title = {Response Generation by Context-aware Prototype Editing},
author = {Yu Wu and Furu Wei and Shaohan Huang and Yunli Wang and Zhoujun Li and Ming Zhou},
journal= {arXiv preprint arXiv:1806.07042},
year = {2018}
}