rematchka at NADI 2023 shared task: Parameter Efficient tuning for Dialect Identification and Dialect Machine Translation

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Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, and enhancing text-to-speech systems.In this paper, we provide our findings and results in the NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA.The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in the first subtask.Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in the second and third subtasks.The proposed model utilizes parameter-efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.

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