  Intimacy estimation of a given text has recently gained importance due to the
increase in direct interaction of NLP systems with humans. Intimacy is an
important aspect of natural language and has a substantial impact on our
everyday communication. Thus the level of intimacy can provide us with deeper
insights and richer semantics of conversations. In this paper, we present our
work on the SemEval shared task 9 on predicting the level of intimacy for the
given text. The dataset consists of tweets in ten languages, out of which only
six are available in the training dataset. We conduct several experiments and
show that an ensemble of multilingual models along with a language-specific
monolingual model has the best performance. We also evaluate other data
augmentation methods such as translation and present the results. Lastly, we
study the results thoroughly and present some noteworthy insights into this
problem.
