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Existing efforts on text synthesis for code-switching mostly require trainingon code-switched texts in the target language pairs, limiting the deployment ofthe models to cases lacking code-switched data. In this work, we study theproblem of synthesizing code-switched texts for language pairs absent from thetraining data. We introduce GLOSS, a model built on top of a pre-trainedmultilingual machine translation model (PMMTM) with an additionalcode-switching module. This module, either an adapter or extra prefixes, learnscode-switching patterns from code-switched data during training, while theprimary component of GLOSS, i.e., the PMMTM, is frozen. The design of onlyadjusting the code-switching module prevents our model from overfitting to theconstrained training data for code-switching. Hence, GLOSS exhibits the abilityto generalize and synthesize code-switched texts across a broader spectrum oflanguage pairs. Additionally, we develop a self-training algorithm on targetlanguage pairs further to enhance the reliability of GLOSS. Automaticevaluations on four language pairs show that GLOSS achieves at least 55%relative BLEU and METEOR scores improvements compared to strong baselines.Human evaluations on two language pairs further validate the success of GLOSS.

Code-Switched Text Synthesis in Unseen Language Pairs