Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information

摘要

Intent classification and slot filling are two critical subtasks of natural language understanding (NLU) in task-oriented dialogue systems. Previous work has made use of either hierarchical or contextual information when jointly modeling intent classification and slot filling, proving that either of them is helpful for joint models. This paper proposes a cluster of joint models to encode both types of information at the same time. Experimental results on different datasets show that the proposed models outperform joint models without either hierarchical or contextual information. Besides, finding the balance between two loss functions of two subtasks is important to achieve best overall performances.