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

04/16/2021 ∙ by Akhila Yerukola, et al. ∙ 8

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.