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The work is intended to tackle a vital field that lies at the intersection of speech processing and natural language processing: Spoken Language Understanding (SLU).
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Its idea is to understand the essence of machine-directed human speech to facilitate its further processing and take on board its cognitive impact.
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The proposed system is CIDIS - Concurrent Intelligent Model for Dialogue Act Classification, Intent Detection, and Slot Filling, which uses a deep concurrent multi-task paradigm to perform the three fundamental tasks of the SLU domain: Dialogue Act Classification, Intent Detection and Slot Filling.
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Since the model is orchestrated in a multi-task fashion, every task interacts with the other to have a global understanding of the input query.
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It follows an intelligent encoding strategy involving concatenation of the query’s BERT and CharCNN embedding to handle all possible edge cases and ambiguities involved in human speech queries.
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This intelligent encoding is passed through a Stacked Bi-LSTM architecture followed by task-specific attention layers.
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The three supplementary outputs are in turn fed to the final module that generates the expected query response in real-time based on the dialogue act, intent, and slot.
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The developed models are evaluated against standard benchmark datasets like ATIS, TRAINS, and FRAMES and the achieved state-of-the-art performances are eventually tabulated.
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This work has been presented at the 2022 IEEE World Conference on Applied Intelligence and Computing and is accessible at [Link to Paper]
