2021
DOI: 10.48550/arxiv.2108.08451
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Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models

Haitao Lin,
Lu Xiang,
Yu Zhou
et al.

Abstract: Spoken Language Understanding (SLU) is one essential step in building a dialogue system. Due to the expensive cost of obtaining the labeled data, SLU suffers from the data scarcity problem. Therefore, in this paper, we focus on data augmentation for slot filling task in SLU. To achieve that, we aim at generating more diverse data based on existing data. Specifically, we try to exploit the latent language knowledge from pretrained language models by finetuning them. We propose two strategies for finetuning proc… Show more

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