@InProceedings{zhang-EtAl:2018:Long1,
  author    = {Zhang, Ruqing  and  Guo, Jiafeng  and  Fan, Yixing  and  Lan, Yanyan  and  Xu, Jun  and  Cheng, Xueqi},
  title     = {Learning to Control the Specificity in Neural Response Generation},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1108--1117},
  abstract  = {In conversation, a general response (e.g., ''I don't know'') could correspond to a large variety of input utterances. Previous generative conversational models usually employ a single model to learn the relationship between different utterance-response pairs, thus tend to favor general and trivial responses which appear frequently. To address this problem, we propose a novel controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. Specifically, we introduce an explicit specificity control variable into a sequence-to-sequence model, which interacts with the usage representation of words through a Gaussian Kernel layer, to guide the model to generate responses at different specificity levels. We describe two ways to acquire distant labels for the specificity control variable in learning. Empirical studies show that our model can significantly outperform the state-of-the-art response generation models under both automatic and human evaluations.},
  url       = {http://www.aclweb.org/anthology/P18-1102}
}