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In multilingual colloquial settings, it is a habitual occurrence to composeexpressions of text or speech containing tokens or phrases of differentlanguages, a phenomenon popularly known as code-switching or code-mixing (CMX).We present our approach and results for the Code-mixed Machine Translation(MixMT) shared task at WMT 2022: the task consists of two subtasks, monolingualto code-mixed machine translation (Subtask-1) and code-mixed to monolingualmachine translation (Subtask-2). Most non-synthetic code-mixed data are fromsocial media but gathering a significant amount of this kind of data would belaborious and this form of data has more writing variation than other domains,so for both subtasks, we experimented with data schedules for out-of-domaindata. We jointly learn multiple domains of text by pretraining and fine-tuning,combined with a sentence alignment objective. We found that switching betweendomains caused improved performance in the domains seen earliest duringtraining, but depleted the performance on the remaining domains. A continuoustraining run with strategically dispensed data of different domains showed asignificantly improved performance over fine-tuning.

Domain Curricula for Code-Switched MT at MixMT 2022