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- W4287690141 abstract "Spoken language understanding (SLU) refers to the process of inferring the semantic information from audio signals. While the neural transformers consistently deliver the best performance among the state-of-the-art neural architectures in field of natural language processing (NLP), their merits in a closely related field, i.e., spoken language understanding (SLU) have not beed investigated. In this paper, we introduce an end-to-end neural transformer-based SLU model that can predict the variable-length domain, intent, and slots vectors embedded in an audio signal with no intermediate token prediction architecture. This new architecture leverages the self-attention mechanism by which the audio signal is transformed to various sub-subspaces allowing to extract the semantic context implied by an utterance. Our end-to-end transformer SLU predicts the domains, intents and slots in the Fluent Speech Commands dataset with accuracy equal to 98.1 %, 99.6 %, and 99.6 %, respectively and outperforms the SLU models that leverage a combination of recurrent and convolutional neural networks by 1.4 % while the size of our model is 25% smaller than that of these architectures. Additionally, due to independent sub-space projections in the self-attention layer, the model is highly parallelizable which makes it a good candidate for on-device SLU." @default.
- W4287690141 created "2022-07-26" @default.
- W4287690141 creator A5023415990 @default.
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- W4287690141 date "2020-08-12" @default.
- W4287690141 modified "2023-09-28" @default.
- W4287690141 title "End-to-End Neural Transformer Based Spoken Language Understanding" @default.
- W4287690141 doi "https://doi.org/10.48550/arxiv.2008.10984" @default.
- W4287690141 hasPublicationYear "2020" @default.
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