TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
Question Similarity	Q2Q Arabic Benchmark	Ensemble multilingual BERT model	F1 score	0.95924	# 1

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The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic

NSURL 2019 · Hana Al-Theiabat, Aisha Al-Sadi ·

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.