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Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance.
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Modeling the context of a target word is of fundamental importance in predicting the semantic label for slot filling task in Spoken Language Understanding (SLU).
Slot Filling
The goal of **Slot Filling** is to identify from a running dialog different slots, which correspond to different parameters of the user’s query.
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Slot filling — A first step towards ambitious NLP systems
Slot filling - You can use slot filling within a scene to collect multiple pieces of typed user input, without having to create multiple intents.
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Slot filling is more precisely giving the slot a value of a type matching the label. For ex- ample, a slot labelled “B-city” could be filled with the value “Sydney”.
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Slot filling is a natural language understanding concept that means saving an extracted entity to an object. However, in Copilot Studio, slot filling means placing the extracted entity value into a variable.