2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-6.2
Paper Title END-TO-END SPOKEN LANGUAGE UNDERSTANDING USING TRANSFORMER NETWORKS AND SELF-SUPERVISED PRE-TRAINED FEATURES
Authors Edmilson Morais, Hong Kwang J Kuo, Samuel Thomas, IBM, Brazil
SessionHLT-6: Language Understanding 2: End-to-end Speech Understanding 2
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Transformer networks and self-supervised pre-training have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of spoken language understanding (SLU) still need further investigation. In this paper we introduce a modular End-to-End (E2E) SLU transformer network based architecture which allows the use of self-supervised pre-trained acoustic features, pre-trained model initialization and multi-task training. Several SLU experiments for predicting intent and entity labels/values using the ATIS dataset are performed. These experiments investigate the interaction of pre-trained model initialization and multi-task training with either traditional filterbank or self-supervised pre-trained acoustic features. Results show not only that self-supervised pre-trained acoustic features outperform filterbank features in almost all the experiments, but also that when these features are used in combination with multi-task training, they almost eliminate the necessity of pre-trained model initialization.