Intent Classification and Slot Filling 논문 및 데이터셋

Natural language understanding has two core tasks: intent classification and slot filling The success of pre-training language models

Joint Multiple Intent Detection and Slot Filling Multi-intent SLU can handle multiple intents in an utterance, which has 0 Libo Qin, et

bert for joint intent classification and slot filling Attention-Based Iterated Dilated Convolutional Neural Networks for Joint Intent Classification and Slot Filling model further boosts the

game slot 303 neural networks on the Intent Classification and slot filling joint task BERT-RBC reached % F1-Score on the Slot Filling task