The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
This work addresses semantic question similarity for under-resourced languages like Arabic, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of detecting semantically similar questions in Arabic by exploring different methods, achieving F1-scores ranging from 88% to 96%, with an ensemble model using pre-trained multilingual BERT achieving 95.924% F1-score and ranking first among nine teams.
This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this work. The proposed models achieved high F1-scores, which range from (88% to 96%). Our official best result is produced from the ensemble model of using a pre-trained multilingual BERT model with different random seeds with 95.924% F1-Score, which ranks the first among nine participants teams.