Identifying Nuanced Dialect for A rabic Tweets with Deep Learning and Reverse Translation Corpus Extension System (2020)

Abstract: In this paper, we present our work for the NADI Shared Task (Abdul-Mageed and Habash, 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 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 us as the 7th out of 18 teams in the final ranking Leaderboard.

## introduction arabic is one of the most complex languages, which presents significant challenges for natural language processing. like other languages, arabic has a number of dialectal varieties. many of these varieties of arabic have started being widely represented in the written form with the emergence of social media. arabic language speakers use modern standard arabic (msa) as the official language in very formal situations , while they use an arabic dialect for everyday conversation. dialect identification is the task of detecting the source variety of a given text or speech segment automatically. previous work on arabic dialect identification has focused on country-level varieties such as the arabic fine-grained dialect identification task (madar) co-located with the fourth arabic natural language processing workshop (wanlp 2019) @xcite . the classification task remains challenging as it covers 21 different arabic dialects with high similarities and common words. throughout the paper, we propose an approach for data balancing and augmentation without using any external manually-labelled data sets. we also report the different systems that were experimented in feature extraction and word embedding such as term frequency-inverse document frequency (tf-idf) and fasttext @xcite . for the tweets classification, logistic regression, bi-directional long short term memory (lstm) @xcite and arabert were evaluated to reach the top score. ## results and discussion interesting observations at the beginning of conducting our work showed that tf-idf and a simple logistic regression model performed better than nn-based models. however, with more experiments, nn-based models outperformed it. the results of the approaches described in the previous section are shown in ## conclusion we introduced a reverse arabic translation solution to handle unbalanced data and small data set, a hierarchical architecture to enhance the efficiency and deal with the 21 classes classification, several neural network based models built on different word and document representations. future work will include trying to ensemble the mentioned models, enhance the two-level hierarchical prediction structure and exploring the effect of adding a named entity recognition system module for better focus on highly effective words that identifies each country such as places, food, public figures, etc. moreover, we will examine more data augmentation methods such as suggested in @xcite @xcite .