%0 Conference Proceedings %T A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling %A E, Haihong %A Niu, Peiqing %A Chen, Zhongfu %A Song, Meina %Y Korhonen, Anna %Y Traum, David %Y Màrquez, Lluís %S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %D 2019 %8 July %I Association for Computational Linguistics %C Florence, Italy %F e-etal-2019-novel %X 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. %R 10.18653/v1/P19-1544 %U https://aclanthology.org/P19-1544 %U https://doi.org/10.18653/v1/P19-1544 %P 5467-5471