  In multilingual colloquial settings, it is a habitual occurrence to compose
expressions of text or speech containing tokens or phrases of different
languages, 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, monolingual
to code-mixed machine translation (Subtask-1) and code-mixed to monolingual
machine translation (Subtask-2). Most non-synthetic code-mixed data are from
social media but gathering a significant amount of this kind of data would be
laborious and this form of data has more writing variation than other domains,
so for both subtasks, we experimented with data schedules for out-of-domain
data. We jointly learn multiple domains of text by pretraining and fine-tuning,
combined with a sentence alignment objective. We found that switching between
domains caused improved performance in the domains seen earliest during
training, but depleted the performance on the remaining domains. A continuous
training run with strategically dispensed data of different domains showed a
significantly improved performance over fine-tuning.
