@inproceedings{e-etal-2019-novel,
    title = "A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling",
    author = "E, Haihong  and
      Niu, Peiqing  and
      Chen, Zhongfu  and
      Song, Meina",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-1544/",
    doi = "10.18653/v1/P19-1544",
    pages = "5467--5471",
    abstract = "A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79{\%} and 5.42{\%} on ATIS and Snips datasets, respectively, compared to the state-of-the-art model."
}