  This study investigates machine translation between related languages i.e.,
languages within the same family that share linguistic characteristics such as
word order and lexical similarity. Machine translation through few-shot
prompting leverages a small set of translation pair examples to generate
translations for test sentences. This procedure requires the model to learn how
to generate translations while simultaneously ensuring that token ordering is
maintained to produce a fluent and accurate translation. We propose that for
related languages, the task of machine translation can be simplified by
leveraging the monotonic alignment characteristic of such languages. We
introduce DecoMT, a novel approach of few-shot prompting that decomposes the
translation process into a sequence of word chunk translations. Through
automatic and human evaluation conducted on multiple related language pairs
across various language families, we demonstrate that our proposed approach of
decomposed prompting surpasses multiple established few-shot baseline
approaches. For example, DecoMT outperforms the strong few-shot prompting BLOOM
model with an average improvement of 8 chrF++ scores across the examined
languages.
