A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling

ACL 2019  ·  Haihong E, Peiqing Niu, Zhongfu Chen, Meina Song ·

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. read more

PDF Abstract ACL 2019 PDF ACL 2019 Abstract

Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Intent Detection ATIS SF-ID (BLSTM) network Accuracy 97.76 # 5
F1 95.80 # 4
Intent Detection ATIS SF-ID Accuracy 97.76 # 5
Slot Filling ATIS SF-ID F1 0.958 # 2
Intent Detection SNIPS SF-ID (BLSTM) network Intent Accuracy 97.43 # 4
Slot F1 Score 92.23 # 4
Intent Detection SNIPS SF-ID Intent Accuracy 97.43 # 4
Slot F1 Score 92.23 # 4

Methods


No methods listed for this paper. Add relevant methods here