@InProceedings{lei-EtAl:2018:Long,
  author    = {Lei, Wenqiang  and  Jin, Xisen  and  Kan, Min-Yen  and  Ren, Zhaochun  and  He, Xiangnan  and  Yin, Dawei},
  title     = {Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures},
  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     = {1437--1447},
  abstract  = {Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.},
  url       = {http://www.aclweb.org/anthology/P18-1133}
}