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arXiv · 2112.06327

Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks

Chia-Yu Li·Ngoc Thang Vu
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Abstract

This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity. We investigate methods to augment Code-switching training text data by artificially generating them. Concretely, we propose a cycle-consistent adversarial networks based framework to transfer monolingual text into Code-switching text, considering Code-switching as a speaking style. Our experimental results on the SEAME corpus show that utilising artificially generated Code-switching text data improves consistently the language model as well as the automatic speech recognition performance.

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Chia-Yu Li, Ngoc Thang Vu. 2021-12-12. Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks. https://arxiv.org/abs/2112.06327

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