Name: ALUE
Description: 8 carefully selected and previously
  published tasks
Provider: Mawdoo3
Paper Title: ALUE: Arabic Language Understanding Evaluation
  
Tasks: irony detection, dialect identification, semantic question similarity, offensive language detection, emotion classification, sentiment intensity regression, entailment
Venue Name: Arabic Natural Language Processing Workshop
Authors: Haitham Seelawi,Ibraheem Tuffaha,Mahmoud Gzawi,Wael Farhan,Bashar Talafha,Riham Badawi,Zyad Sober,Oday Al-Dweik,Abed Alhakim Freihat,Hussein T. Al-Natsheh
Abstract: The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU). We strongly believe thatmany NLU problems in Arabic are especiallypoised to reap the benefits of such models. Tothis end we propose the Arabic Language Un-derstanding Evaluation Benchmark (ALUE),based on 8 carefully selected and previouslypublished tasks. For five of these, we providenew privately held evaluation datasets to en-sure the fairness and validity of our benchmark.We also provide a diagnostic dataset to helpresearchers probe the inner workings of theirmodels.Our initial experiments show thatMTL models outperform their singly trainedcounterparts on most tasks. But in order to en-tice participation from the wider community,we stick to publishing singly trained baselinesonly. Nonetheless, our analysis reveals thatthere is plenty of room for improvement inArabic NLU. We hope that ALUE will playa part in helping our community realize someof these improvements. Interested researchersare invited to submit their results to our online,and publicly accessible leaderboard.