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Found 3 papers, 2 papers with code
Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages
In the on-device scenario (tiny and not pretrained SLU), our method improved the Overall Accuracy from 5. 31% to 22. 06% over the baseline Global-Local Contrastive Learning Framework (GL-CLeF) method.
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models.
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?
In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model.
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