  This paper describes the system description for the HinglishEval challenge at
INLG 2022. The goal of this task was to investigate the factors influencing the
quality of the code-mixed text generation system. The task was divided into two
subtasks, quality rating prediction and annotators disagreement prediction of
the synthetic Hinglish dataset. We attempted to solve these tasks using
sentence-level embeddings, which are obtained from mean pooling the
contextualized word embeddings for all input tokens in our text. We
experimented with various classifiers on top of the embeddings produced for
respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on
subtask A.
