意图识别与语义槽填充的双向关联模型

意图识别与语义槽填充联合建模正成为口语理解（Spoken Language Understanding，SLU）的新趋势。但是，现有的联合模型只是简单地将两个任务进行关联，建立了两任务间的单向联系，未充分利用两任务之间的关联关系。考虑到意图识别与语义槽填充的双向关联关系可以使两任务相互促进，提出了一种基于门控机制的双向关联模型（BiAss-Gate），将两个任务的上下文信息进行融合，深度挖掘意图识别与语义槽填充之间的联系，从而优化口语理解的整体性能。实验表明，所提模型BiAss-Gate在ATIS和Snips数据集上，语义槽填充F1值最高达95.8%，意图识别准确率最高达98.29%，对比其他模型性能得到了显著提升。

Bidirectional Association Model for Intent Detection and Slot Filling

The joint model of intent detection and slot filling is becoming a new trend in spoken language understanding.The existing joint model simply associates two tasks, a unidirectional relationship between the two tasks is established. However, the relationship between the two tasks is not fully utilized. Considering that the bidirectional association relation between intention detection and slot filling can promote each other,a bidirectional association model based on gate mechanism（BiAss-Gate） is proposed. BiAss-Gate model explores deeply the association between intent detection and slot filling by combining the context information of two task,it optimizes the global performance of spoken language understanding.Experimental results show that the BiAss-Gate model has the highest F1 value of 95.8% in the ATIS datasets and the highest intent recognition detection of 98.29% in Snips datasets.The performance is significantly improved compared with other models.