  Formality plays a significant role in language communication, especially in
low-resource languages such as Hindi, Japanese and Korean. These languages
utilise formal and informal expressions to convey messages based on social
contexts and relationships. When a language translation technique is used to
translate from a source language that does not pertain the formality (e.g.
English) to a target language that does, there is a missing information on
formality that could be a challenge in producing an accurate outcome. This
research explores how this issue should be resolved when machine learning
methods are used to translate from English to languages with formality, using
Hindi as the example data. This was done by training a bilingual model in a
formality-controlled setting and comparing its performance with a pre-trained
multilingual model in a similar setting. Since there are not a lot of training
data with ground truth, automated annotation techniques were employed to
increase the data size. The primary modeling approach involved leveraging
transformer models, which have demonstrated effectiveness in various natural
language processing tasks. We evaluate the official formality accuracy(ACC) by
comparing the predicted masked tokens with the ground truth. This metric
provides a quantitative measure of how well the translations align with the
desired outputs. Our study showcases a versatile translation strategy that
considers the nuances of formality in the target language, catering to diverse
language communication needs and scenarios.
