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Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English.
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.
Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image.