In this presentation, we will present our approach to taking Custom NMT to the next level by building tailor-made NMT to fit the needs of businesses seeking to scale in the Arabic-speaking world. In close collaboration with customers in the MENA region and with a deep understanding of their data, we work on building a variety of NMT models that accommodate to the unique challenges of the Arabic language. This session will provide insights into the challenges of acquiring, analyzing, and processing customer data in various sectors, as well as insights into how to best make use of this data to build high-quality Custom NMT models in English-Arabic. Feedback from usage of these models in production will be provided. Furthermore, we will show how to use our translation management system to make the most of the custom NMT, by leveraging the models, fine-tuning and continuing to improve them over time.

In this paper, we discuss our team’s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17% F1-score and 35% accuracy.

There is a growing research interest in studying word similarity. Without a doubt, two similar words in a context may considered different in another context. Therefore, this paper investigates the effect of the context in word similarity. The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity). In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian. Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pretrained embeddings techniques for each of the four languages. Our proposed model outperformed the baseline models in both subtasks and acheived the best result for subtask 2 in English and Finnish, with score 0.725 and 0.68 respectively. We have been ranked the sixth for subtask 1, with scores for English, Croatian, Finnish, and Slovenian as follows: 0.738, 0.44, 0.546, 0.512.