@article{2990,
  author = {Lingfeng Pan, Yi Zhang, Feiliang Ren, Yining Hou, Yan Li, Xiaobo Liang, Yongkang Liu},
  title = {A Multiple Utterances based Neural Network Model for Joint Intent Detection and Slot Filling},
  journal = {Journal of Information Security Research},
  year = {2020},
  volume = {11},
  number = {2},
  doi = {https://doi.org/10.6025/jisr/2020/11/2/54-60},
  url = {https://www.dline.info/jisr/fulltext/v11n2/jisrv11n2_3.pdf},
  abstract = {Spoken language understanding(SLU), which usually involves slot filling and intent detection, is an important
task in natural language processing. Most of the state-of-the-art methods are usually take single utterance as input, which
would introduce much ambiguity because the loss of context information. To address this issue, we propose a new neural
network based joint intent detection and slot filling model which takes multiple utterances as input. In our method, we use an
utterance2utterance attention mechanism to combine the information of multiple continuous utterances. We also combine the
intent information to the slot filling process with a gating mechanism. Using this proposed model, we participated in the task2
of CCKS2018. Finally, our model ranks NO.2 among the hugely competitive models.},
}