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

• URL: http://arxiv.org/abs/2112.06327v1
• Date: Sun, 12 Dec 2021 21:27:32 GMT
• Title: Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks
• Authors: Chia-Yu Li and Ngoc Thang Vu
• Abstract summary: We investigate methods to augment Code-switching training text data by artificially generating them. We propose a cycle-consistent adversarial networks based framework to transfer monolingual text into Code-switching text.
• Score: 41.88097793717185
• License: http://creativecommons.org/licenses/by/4.0/
• 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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