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• W4319862410 abstract "Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming. Recent studies achieved promising results by using pre-trained models in low-resource scenarios. Inspired by this, we aim to ask: which (if any) pre-training strategies can improve performance across SLU benchmarks? To answer this question, we employ four types of pre-trained models and their combinations for SLU. We leverage self-supervised speech and language models (LM) pre-trained on large quantities of un-paired data to extract strong speech and text representations. We also explore using supervised models pre-trained on larger external automatic speech recognition (ASR) or SLU corpora. We conduct extensive experiments on the SLU Evaluation (SLUE) benchmark and observe self-supervised pre-trained models to be more powerful, with pre-trained LM and speech models being most beneficial for the Sentiment Analysis and Named Entity Recognition task, respectively. <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> Our code and models will be publicly available as part of the ESPnet-SLU toolkit." @default.
• W4319862410 created "2023-02-11" @default.
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• W4319862410 date "2023-01-09" @default.
• W4319862410 modified "2023-10-01" @default.
• W4319862410 title "A Study on the Integration of Pre-Trained SSL, ASR, LM and SLU Models for Spoken Language Understanding" @default.
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• W4319862410 doi "https://doi.org/10.1109/slt54892.2023.10022399" @default.
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