# The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic Source: [The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:19:08.212Z Authors: Hana Al-Theiabat, Aisha Al-Sadi Published: 2020-04-24T19:52:40.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Question Similarity on NSURL Task 8 Arabic Semantic Question Similarity Public 1.0 (test)](): Semantic Question Similarity; NSURL Task 8 Arabic Semantic Question Similarity Public 1.0 (test); F1 Score: 96.499. - [Semantic Question Similarity on NSURL Task 8 (test 70% private)](): Semantic Question Similarity; NSURL Task 8 Arabic Semantic Question Similarity 1.0 (test 70% private); F1 Score: 96.232. ## Original sources and implementation [Original paper]() [PDF]()