ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding

• URL: http://arxiv.org/abs/2010.12283v2
• Date: Sun, 11 Apr 2021 13:52:26 GMT
• Title: ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding
• Authors: Minjeong Kim, Gyuwan Kim, Sang-Woo Lee, Jung-Woo Ha
• Abstract summary: We introduce a cross-modal pre-trained language model, called Speech-Text BERT (ST-BERT), to tackle end-to-end spoken language understanding tasks. Taking phoneme posterior and subword-level text as an input, ST-BERT learns a contextualized cross-modal alignment. Our method shows further SLU performance gain via domain-adaptive pre-training with domain-specific speech-text pair data.
• Score: 23.367329217151084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language model pre-training has shown promising results in various downstream tasks. In this context, we introduce a cross-modal pre-trained language model, called Speech-Text BERT (ST-BERT), to tackle end-to-end spoken language understanding (E2E SLU) tasks. Taking phoneme posterior and subword-level text as an input, ST-BERT learns a contextualized cross-modal alignment via our two proposed pre-training tasks: Cross-modal Masked Language Modeling (CM-MLM) and Cross-modal Conditioned Language Modeling (CM-CLM). Experimental results on three benchmarks present that our approach is effective for various SLU datasets and shows a surprisingly marginal performance degradation even when 1% of the training data are available. Also, our method shows further SLU performance gain via domain-adaptive pre-training with domain-specific speech-text pair data.

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