Code-switching sentence generation by generative adversarial networks and its application to data augmentation
Journal
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Journal Volume
2019-September
Pages
554-558
Date Issued
2019
Author(s)
Abstract
Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-dependent and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to most monolingual tasks, insufficient data is an issue for code-switching. To mitigate the issue without expensive human annotation, we proposed an unsupervised method for code-switching data augmentation. By utilizing a generative adversarial network, we can generate intra-sentential code-switching sentences from monolingual sentences. We applied the proposed method on two corpora, and the result shows that the generated code-switching sentences improve the performance of code-switching language models.
SDGs
Type
conference paper