Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System

摘要

In this paper,we present our work for the NADI Shared Task (Abdul-Mageed et al.,2020): Nuanced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We introduce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several experimented approaches of word and document representations and different models architectures. The top scoring model was based on the Transformer-based Model for Arabic Language Understanding (AraBERT) (Antoun et al.,2020),with our modified extended corpus based on reverse translation of the given Arabic tweets. The selected system achieved a macro average F1 score of 20.34% on the test set,which places our team CodeLyoko as the 7th out of 18 teams in the final ranking Leaderboard.