A Multiple Utterances based Neural Network Model for Joint Intent Detection and Slot Filling
Spoken language understanding(SLU) which usually involves slot filling and intent detection, is an important task in natural language processing. Most of the state of the art methods are usually take single utterance as input, which would introduce much ambiguity because the loss of context information. To address this issue, we propose a new neural network based joint intent detection and slot filling model which takes multiple utterances as input. In our method, we use an utterance2utterance attention mechanism to combine the information of multiple continuous utterances. We also combine the intent information to the slot filling process with a gating mechanism. Using this proposed model, we participated in the task2 of CCKS2018. Finally, our model ranks NO.2 among the hugely competitive models.
CCKS Tasks