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arXiv · 2104.08268

Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase

Akhila Yerukola·Mason Bretan·Hongxia Jin
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Abstract

We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method with a range of augmentation techniques encompassing generative models such as VAEs and performance-boosting techniques such as synonym replacement and back-translation. We show our method performs strongly on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity.

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BibTeXRIS

Akhila Yerukola, Mason Bretan, Hongxia Jin. 2021-04-16. Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase. https://arxiv.org/abs/2104.08268

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